ST.ANNES COLLEGE OF ENGINEERING AND TECHNOLOGY

ANGUCHETTYPALAYAM, PANRUTI - 607 110

CS1002 – DIGITAL IMAGE PROCESSING

(FOR IV B.E ELECTRONICS AND COMMUNICATION ENGINEERING)

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

PREPARED BY: Mr. R. ANNAMALAI

QUESTION BANK

CS1002 DIP 2 MARK

ST. ANNE’S COLLEGE OF ENNGINEERING & TECHNOLOGY

ANGUCHETTYPALAYAM, PANRUTI-607 110 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

CS1002 DIGITAL IMAGE PROCESSING QUESTION BANK

YEAR : IV SEM:VIII

UNIT I DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS

1. Define Image?

An image may be defined as two dimensional light intensity function f(x, y) where x and y denote

spatial co-ordinate and the amplitude or value of f at any point (x, y) is called intensity or grayscale or

brightness of the image at that point.

2. What is Dynamic Range? The range of values spanned by the gray scale is called dynamic range of an image. Image will

have high contrast, if the dynamic range is high and image will have dull washed out gray look if the

dynamic range is low.

3. Define Brightness? Brightness of an object is the perceived luminance of the surround. Two objects with different

surroundings would have identical luminance but different brightness.

4. Define Tapered Quantization? If gray levels in a certain range occur frequently while others occurs rarely, the quantization

levels are finely spaced in this range and coarsely spaced outside of it. This method is sometimes called

Tapered Quantization.

5. What do you meant by Gray level? Gray level refers to a scalar measure of intensity that ranges from black to grays and finally to

white.

6. What do you meant by Color model? A Color model is a specification of 3D-coordinates system and a subspace within that system

where each color is represented by a single point.

7. List the hardware oriented color models? 1. RGB model

2. CMY model

3. YIQ model

4. HSI model

8. What is Hue of saturation? Hue is a color attribute that describes a pure color where saturation gives a measure of the degree

to which a pure color is diluted by white light.

9. List the applications of color models? 1. RGB model--- used for color monitor & color video camera

2. CMY model---used for color printing

3. HIS model----used for color image processing

4. YIQ model---used for color picture transmission

10. What is Chromatic Adoption? The hue of a perceived color depends on the adoption of the viewer. For example, the American

Flag will not immediately appear red, white, and blue of the viewer has been subjected to high intensity

red light before viewing the flag. The color of the flag will appear to shift in hue toward the red

component cyan.

11. Define Resolutions? Resolution is defined as the smallest number of discernible detail in an image.Spatial resolution is

the smallest discernible detail in an image and gray level resolution refers to the smallest discernible

change is gray level.

CS1002 DIP 2 MARK

12. What is meant by pixel? A digital image is composed of a finite number of elements each of which has a particular

location or value. These elements are referred to as pixels or image elements or picture elements or pels

elements.

13. Define Digital image? When x, y and the amplitude values of f all are finite discrete quantities , we call the image digital

image.

14. What are the steps involved in DIP? 1. Image Acquisition

2. Preprocessing

3. Segmentation

4. Representation and Description

5. Recognition and Interpretation

15. What is recognition and Interpretation? Recognition means is a process that assigns a label to an object based on the information provided

by its descriptors. Interpretation means assigning meaning to a recognized object.

16. Specify the elements of DIP system? 1. Image Acquisition

2. Storage

3. Processing

4. Display

17. Explain the categories of digital storage? 1. Short term storage for use during processing.

2. Online storage for relatively fast recall.

3. Archical storage for infrequent access. 18. What are the types of light receptors?

The two types of light receptors are

1. Cones and

2. Rods

19. Differentiate photopic and scotopic vision? Photopic vision Scotopic vision

1. The human being can resolve the fine details with these cones because each one is connected to its

ownnerve end.

2. This is also known as bright light vision. Several rods are connected to one nerve end. So it gives the

overall picture of the image. This is also known as thin light vision.

20. How cones and rods are distributed in retina? In each eye, cones are in the range 6-7 million and rods are in the range 75-150 million.

21. Define subjective brightness and brightness adaptation? Subjective brightness means intensity as preserved by the human visual system.Brightness

adaptation means the human visual system can operate only from scotopic to glare limit. It cannot operate

over the range simultaneously. It accomplishes this large variation by changes in its overall intensity.

22. Define weber ratio The ratio of increment of illumination to background of illumination is called as weber ratio.(ie)

_i/i

If the ratio (_i/i) is small, then small percentage of change in intensity is needed (ie) good

brightness adaptation.

If the ratio (_i/i) is large , then large percentage of change in intensity is needed (ie) poor

brightness adaptation.

23. What is meant by machband effect? Machband effect means the intensity of the stripes is constant. Therefore it preserves the

brightness pattern near the boundaries, these bands are called as machband effect.

CS1002 DIP 2 MARK

24. What is simultaneous contrast? The region reserved brightness not depend on its intensity but also on its background. All centre

square have same intensity. However they appear to the eye to become darker as the background becomes

lighter.

25. What is meant by illumination and reflectance? Illumination is the amount of source light incident on the scene. It is represented as i(x, y).

Reflectance is the amount of light reflected by the object in the scene. It is represented by r(x, y).

26. Define sampling and quantization?

Sampling means digitizing the co-ordinate value (x, y).Quantization means digitizing the

amplitude value.

27. Find the number of bits required to store a 256 X 256 image with 32 gray levels? 32 gray levels = 25 = 5 bits 256 * 256 * 5 = 327680 bits.

28. Write the expression to find the number of bits to store a digital image? The number of bits required to store a digital image is b=M X N X k ,When M=N, this equation

becomes b=N^2k

30. What do you meant by Zooming of digital images? Zooming may be viewed as over sampling. It involves the creation of new pixel locations and the

assignment of gray levels to those new locations.

31. What do you meant by shrinking of digital images? Shrinking may be viewed as under sampling. To shrink an image by one half, we delete every

row and column. To reduce possible aliasing effect, it is a good idea to blue an image slightly before

shrinking it.

32. Write short notes on neighbors of a pixel. The pixel p at co-ordinates (x, y) has 4 neighbors (ie) 2 horizontal and 2 vertical neighbors whose

co-ordinates is given by (x+1, y), (x-1,y), (x,y-1), (x, y+1). This is called as direct neighbors. It is denoted

by N4(P) Four diagonal neighbors of p have co-ordinates (x+1, y+1), (x+1,y-1), (x-1, y-1), (x-1, y+1). It

is denoted by ND(4). Eight neighbors of p denoted by N8(P) is a combination of 4 direct neighbors and 4

diagonal neighbors.

33. Explain the types of connectivity. 1. 4 connectivity

2. 8 connectivity

3. M connectivity (mixed connectivity)

34. What is meant by path? Path from pixel p with co-ordinates (x, y) to pixel q with co-ordinates (s,t) is a sequence of

distinct pixels with co-ordinates.

35. Give the formula for calculating D4 and D8 distance.

D4 distance ( city block distance) is defined by D4(p, q) = |x-s| + |y-t| D8 distance(chess board distance) is

defined by D8(p, q) = max(|x-s|, |y-t|).

36. What is geometric transformation? Transformation is used to alter the co-ordinate description of image.

The basic geometric transformations are

1. Image translation

2. Scaling

3. Image rotation

37. What is image translation and scaling? Image translation means reposition the image from one co-ordinate location to another along

straight line path.

Scaling is used to alter the size of the object or image (ie) a co-ordinate system is scaled by a

factor.

CS1002 DIP 2 MARK

38. What is the need for transform? The need for transform is most of the signals or images are time domain signal (ie) signals can be

measured with a function of time. This representation is not always best. For most image processing

applications anyone of the mathematical transformation are applied to the signal or images to obtain

further information from that signal.

39. Define the term Luminance? Luminance measured in lumens (lm), gives a measure of the amount of energy an observer

perceiver from a light source.

40. What is Image Transform? An image can be expanded in terms of a discrete set of basis arrays called basis images. These

basis images can be generated by unitary matrices. Alternatively, a given NxN image can be viewed as an

N^2x1 vectors. An image transform provides a set of coordinates or basis vectors for vector space.

41. What are the applications of transform. 1) To reduce band width

2) To reduce redundancy

3) To extract feature.

42. Give the Conditions for perfect transform? Transpose of matrix = Inverse of a matrix. Orthoganality.

43. What are the properties of unitary transform? 1) Determinant and the Eigen values of a unitary matrix have unity magnitude

2) the entropy of a random vector is preserved under a unitary Transformation

3) Since the entropy is a measure of average information, this means information is preserved under a

unitary transformation. 44. Define fourier transform pair?

The fourier transform of f(x) denoted by F(u) is defined by F(u)= _ f(x) e-j2_ux dx -------------(1)

The inverse fourier transform of f(x) is defined by f(x)= _F(u) ej2_ux dx --------------------(2)

The equations (1) and (2) are known as fourier transform pair.

45. Define fourier spectrum and spectral density? Fourier spectrum is defined as F(u) = |F(u)| e j_(u)

Where

|F(u)| = R2(u)+I2(u)

_(u) = tan-1(I(u)/R(u))

Spectral density is defined by

p(u) = |F(u)|2

p(u) = R2(u)+I2(u)

46. Give the relation for 1-D discrete fourier transform pair? The discrete fourier transform is defined by n-1 F(u) = 1/N _ f(x) e –j2_ux/N x=0

The inverse discrete fourier transform is given by n-1 f(x) = _ F(u) e j2_ux/N x=0

These equations are known as discrete fourier transform pair.

47. Specify the properties of 2D fourier transform. The properties are

1. Separability

2. Translation

3. Periodicity and conjugate symmetry

4. Rotation

5. Distributivity and scaling

6. Average value

7. Laplacian

8. Convolution and correlation

9. sampling

CS1002 DIP 2 MARK

48. Explain separability property in 2D fourier transform The advantage of separable property is that F(u, v) and f(x, y) can be obtained by successive

application of 1D fourier transform or its inverse. n-1 F(u, v) =1/N _ F(x, v) e –j2_ux/N x=0

Where n-1 F(x, v)=N[1/N _ f(x, y) e –j2_vy/N y=0

49. Properties of twiddle factor. 1. Periodicity WN^(K+N)= WN^K

2. Symmetry WN^(K+N/2)= -WN^K

50. Give the Properties of one-dimensional DFT 1. The DFT and unitary DFT matrices are symmetric.

2. The extensions of the DFT and unitary DFT of a sequence and their inverse transforms are

periodic with period N.

3. The DFT or unitary DFT of a real sequence is conjugate symmetric about N/2.

51. Give the Properties of two-dimensional DFT 1. Symmetric

2. Periodic extensions

3. Sampled Fourier transform

4. Conjugate symmetry.

52. What is meant by convolution? The convolution of 2 functions is defined by f(x)*g(x) = f() .g(x- ) d where is the dummy

variable

53. State convolution theorem for 1D If f(x) has a fourier transform F(u) and g(x) has a fourier transform G(u) then f(x)*g(x) has a

fourier transform F(u).G(u).

Convolution in x domain can be obtained by taking the inverse fourier transform of the product

F(u).G(u).Convolution in frequency domain reduces the multiplication in the x

domain F(x).g(x) _ F(u)* G(u)

These 2 results are referred to the convolution theorem.

54. What is wrap around error? The individual periods of the convolution will overlap and referred to as wrap around error

55. Give the formula for correlation of 1D continuous function. The correlation of 2 continuous functions f(x) and g(x) is defined by f(x) o g(x) = f*() g(x+)

d

56. What are the properties of Haar transform. 1. Haar transform is real and orthogonal.

2. Haar transform is a very fast transform

3. Haar transform has very poor energy compaction for images

4. The basic vectors of Haar matrix sequensly ordered.

57. What are the Properties of Slant transform 1. Slant transform is real and orthogonal.

2. Slant transform is a fast transform

3. Slant transform has very good energy compaction for images

4. The basic vectors of Slant matrix are not sequensely ordered.

58. Specify the properties of forward transformation kernel? The forward transformation kernel is said to be separable if g(x, y, u, v) g(x, y, u, v) = g1(x,

u).g2(y, v) The forward transformation kernel is symmetric if g1 is functionally equal to g2 g(x, y, u, v) =

g1(x, u). g1(y,v)

59. Define fast Walsh transform. The Walsh transform is defined by n-1 x-1 w(u) = 1/N _ f(x) _ (-1) bi(x).bn-1-i (u) x=0 i=0

60. Give the relation for 1-D DCT. The 1-D DCT is, N-1 C(u)=_(u)_ f(x) cos[((2x+1)u_)/2N] where u=0,1,2,….N-1 X=0

N-1 Inverse f(x)= _ _(u) c(u) cos[((2x+1) u_)/2N] where x=0,1,2,…N-1 V=0

CS1002 DIP 2 MARK

61.Write slant transform matrix SN. SN = 1/_2

62. Define Haar transform. The Haar transform can be expressed in matrix form as, T=HFH

Where F = N X N image matrixH = N X N transformation matrix T = resulting N X N transform.

UNIT II

IMAGE ENHANCEMENT TECHNIQUES

1. Specify the objective of image enhancement technique.

The objective of enhancement technique is to process an image so that the result is more suitable

than the original image for a particular application.

2. Explain the 2 categories of image enhancement. i) Spatial domain refers to image plane itself & approaches in this category are based on direct

manipulation of picture image.

ii) Frequency domain methods based on modifying the image by fourier transform.

3. What is contrast stretching? Contrast stretching reduces an image of higher contrast than the original by darkening the levels

below m and brightening the levels above m in the image.

4. What is grey level slicing? Highlighting a specific range of grey levels in an image often is desired. Applications include

enhancing features such as masses of water in satellite imagery and enhancing flaws in x-ray images.

5. Define image subtraction. The difference between 2 images f(x,y) and h(x,y) expressed as, g(x,y)=f(x,y)-h(x,y) is obtained

by computing the difference between all pairs of corresponding pixels from f and h.

6. What is the purpose of image averaging? An important application of image averagingis in the field of astronomy, where imaging with

very low light levels is routine, causing sensor noise frequently to render single images virtually useless

for analysis.

7. What is meant by masking? Mask is the small 2-D array in which the values of mask co-efficient determines the nature of

process.The enhancement technique based on this type of approach is referred to as mask processing.

8. Give the formula for negative and log transformation. Negative: S=L-1-r Log: S = c log(1+r) Where c-constant and r 0

9. What is meant by bit plane slicing? Instead of highlighting gray level ranges, highlighting the contribution made to total image

appearance by specific bits might be desired. Suppose that each pixel in an image is represented by 8 bits.

Imagine that the image is composed of eight 1-bit planes, ranging from bit plane 0 for LSB to bit plane-7

for MSB.

10. Define histogram. The histogram of a digital image with gray levels in the range [0, L-1] is a discrete function

h(rk)=nk. rk-kth gray level nk-number of pixels in the image having gray level rk.

11. What is meant by histogram equalization? k k

Sk= T(rk) = _ Pr(rj) = _ nj/n where k=0,1,2,….L-1

j=0 j=0

This transformation is called histogram equalization.

12. Differentiate linear spatial filter and non-linear spatial filter.

13. Give the mask used for high boost filtering.

CS1002 DIP 2 MARK

14. What is meant by laplacian filter? The laplacian for a function f(x,y) of 2 variables is defined as,

15. Write the steps involved in frequency domain filtering. x+y1. Multiply the input image by (-1) to center the transform.

2. Compute F(u,v), the DFT of the image from (1).

3. Multiply F(u,v) by a filter function H(u,v).

4. Compute the inverse DFT of the result in (3).

5. Obtain the real part of the result in (4). x+y

6. Multiply the result in (5) by (-1)

16. Give the formula for transform function of a Butterworth low pass filter. The transfer function of a Butterworth low pass filter of order n and with cut off frequency at a

distance D0 from the origin is, Where D(u,v) = [(u – M/2) + (v-N/2) ]

17. What do you mean by Point processing? Image enhancement at any Point in an image depends only on the gray level at that point is often

referred to as Point processing.

18. What is Image Negatives? The negative of an image with gray levels in the range [0, L-1] is obtained by using the negative

transformation, which is given by the expression. s = L-1-r Where s is output pixel r is input pixel

19. Define Derivative filter? For a function f (x, y), the gradient f at co-ordinate (x, y) is defined as the vector

20. Explain spatial filtering? Spatial filtering is the process of moving the filter mask from point to point in an image. For

linear spatial filter, the response is given by a sum of products of the filter coefficients, and the

corresponding image pixels in the area spanned by the filter mask.

21. What is a Median filter? The median filter replaces the value of a pixel by the median of the gray levels in the

neighborhood of that pixel.

22. What is maximum filter and minimum filter? The 100th percentile is maximum filter is used in finding brightest points in an image. The 0th

percentile filter is minimum filter used for finding darkest points in an image.

23. Write the application of sharpening filters? 1. Electronic printing and medical imaging to industrial application

2. Autonomous target detection in smart weapons.

24. Name the different types of derivative filters? 1. Perwitt operators

2. Roberts cross gradient operators

3. Sobel operators

UNIT III IMAGE RESTORATION

1. What is meant by Image Restoration? Restoration attempts to reconstruct or recover an image that has been degraded by using a clear

knowledge of the degrading phenomenon.

2. What are the two properties in Linear Operator? Additivity

_ Homogenity

3. Explain additivity property in Linear Operator? H[f1(x,y)+f2(x,y)]=H[f1(x,y)]+H[f2(x,y)]

The additive property says that if H is the linear operator,the response to a sum of two is equal to

the sum of the two responses.

CS1002 DIP 2 MARK

4. How a degradation process is modeled? A system operator H, which together with an additive white noise term _(x,y) a operates on an

input image f(x,y) to produce a degraded image g(x,y).

5. Explain homogenity property in Linear Operator? H[k1f1(x,y)]=k1 H[f1(x,y)]

The homogeneity property says that,the response to a constant multiple of any input is equal to

the response to that input multiplied by the same constant.

6. Give the relation for degradation model for continuous function? g(x,y) =-____f(_,_)§(x-_,y-_).d_d_+_(x,y)

7. What is fredholm integral of first kind? g(x,y) = __f(_,_)h(x,_,y,_)d_ d_

which is called the superposition or convolution or fredholm integral of first kind. It states that if

the response of H to an impulse is known, the response to any input f(_,_) can be calculated by means of

fredholm integral.

8. Define circulant matrix? A square matrix, in which each row is a circular shift of the preceding row and the first row is a

circular shift of the last row, is called circulant matrix.

9. What is concept algebraic approach? The concept of algebraic approach is to estimate the original image which minimizes a predefined

criterion of performances.

10. What are the two methods of algebraic approach? o Unconstraint restoration approach

o Constraint restoration approach

11. Define Gray-level interpolation? Gray-level interpolation deals with the assignment of gray levels to pixels in the spatially

transformed image

12. What is meant by Noise probability density function? The spatial noise descriptor is the statistical behavior of gray level values in the noise component

of the model.

13. Why the restoration is called as unconstrained restoration? In the absence of any knowledge about the noise ‘n’, a meaningful criterion function is to seek an

f^ such that H f^ approximates of in a least square sense by assuming the noise term is as small as

possible. Where H = system operator. f^ = estimated input image. g = degraded image.

14. Which is the most frequent method to overcome the difficulty to formulate the spatial relocation

of pixels? The point is the most frequent method, which are subsets of pixels whose location in the input

(distorted) and output (corrected) imaged is known precisely.

15. What are the three methods of estimating the degradation function? 1. Observation

2. Experimentation

3. Mathematical modeling.

16. What are the types of noise models? Guassian noise

Rayleigh noise

Erlang noise

Exponential noise

Uniform noise_ Impulse noise

17. Give the relation for guassian noise? Guassian noise: The PDF guassian random variable Z is given by P(Z)=e-(Z-μ)2/2_2/_2__

Z->Gray level value _->standard deviation _2->varianze of Z μ->mean of the graylevel value Z

CS1002 DIP 2 MARK

18. Give the relation for rayleigh noise? Rayleigh noise: The PDF is P(Z)= 2(z-a)e-(z—a)2/b/b for Z>=a 0 for Z<a

mean μ=a+__b/4 standard deviation _2=b(4-_)/4

19. Give the relation for Gamma noise? Gamma noise: The PDF is P(Z)=ab zb-1 ae-az/(b-1) for Z>=0 0 for Z<0

mean μ=b/a standard deviation _2=b/a2

20. Give the relation for Exponential noise? Exponential noise The PDF is P(Z)= ae-az Z>=0 0 Z<0

mean μ=1/a standard deviation _2=1/a2

21. Give the relation for Uniform noise? Uniform noise: The PDF is P(Z)=1/(b-a) if a<=Z<=b 0 otherwise

mean μ=a+b/2

standard deviation _2=(b-a)2/12

22. Give the relation for Impulse noise? Impulse noise: The PDF is P(Z) =Pa for z=a Pb for z=b0 Otherwise

23. What is inverse filtering? The simplest approach to restoration is direct inverse filtering, an estimate F^(u,v) of the

transform of the original image simply by dividing the transform of the degraded image G^(u,v) by the

degradation function. F^ (u,v) = G^(u,v)/H(u,v)

24. What is pseudo inverse filter? It is the stabilized version of the inverse filter.For a linear shift invariant system with frequency

response H(u,v) the pseudo inverse filter is defined as H-(u,v)=1/(H(u,v) H=/0 0 H=0

25. What is meant by least mean square filter? The limitation of inverse and pseudo inverse filter is very sensitive noise.The wiener filtering is a

method of restoring images in the presence of blurr as well as noise.

26. Give the equation for singular value decomposition of an image? This equation is called as singular value decomposition of an image.

27. Write the properties of Singular value Decomposition(SVD)? The SVD transform varies drastically from image to image.

The SVD transform gives best energy packing efficiency for any given image.

The SVD transform is useful in the design of filters finding least square,minimum solution of

linear equation and finding rank of large matrices.

28. What is meant by blind image restoration? An information about the degradation must be extracted from the observed image either explicitly

or implicitly.This task is called as blind image restoration.

29. What are the two approaches for blind image restoration? (i) Direct measurement (ii)Indirect estimation

30. What is meant by Direct measurement? In direct measurement the blur impulse response and noise levels are first estimated from an

observed image where this parameter are utilized in the restoration.

31. What is blur impulse response and noise levels? Blur impulse response:

This parameter is measured by isolating an image of a suspected object within a picture.

Noise levels: The noise of an observed image can be estimated by measuring the image covariance over a

region of constant background luminence.

32. What is meant by indirect estimation? Indirect estimation method employ temporal or spatial averaging to either obtain a restoration or

to obtain key elements of an image restoration algorithm.

33. Give the difference between Enhancement and Restoration? Enhancement technique is based primarily on the pleasing aspects it might present to the viewer.

For example: Contrast Stretching. Where as Removal of image blur by applying a deblurrings function is

considered a restoration technique.

CS1002 DIP 2 MARK

UNIT IV IMAGE COMPRESSION

1. What is image compression? Image compression refers to the process of redundancy amount of data required to represent the

given quantity of information for digital image. The basis of reduction process is removal of redundant

data.

2. What is Data Compression? Data compression requires the identification and extraction of source redundancy. In other words,

data compression seeks to reduce the number of bits used to store or transmit information.

3. What are two main types of Data compression? (i) Lossless compression can recover the exact original data after compression. It is used mainly

for compressing database records, spreadsheets or word processing files, where exact replication of the

original is essential.

(ii) Lossy compression will result in a certain loss of accuracy in exchange for a substantial

increase in compression. Lossy compression is more effective when used to compress graphic images and

digitised voice where losses outside visual or aural perception can be tolerated.

4. What is the need for Compression? In terms of storage, the capacity of a storage device can be effectively increased with methods

that compress a body of data on its way to a storage device and decompresses it when it is retrieved. In

terms of communications, the bandwidth of a digital communication link can be effectively increased by

compressing data at the sending end and decompressing data at the receiving end.

At any given time, the ability of the Internet to transfer data is fixed. Thus, if data can effectively

be compressed wherever possible, significant improvements of data throughput can be achieved. Many

files can be combined into one compressed document making sending easier.

5. What are different Compression Methods?

Run Length Encoding (RLE)

Arithmetic coding

Huffman coding and

Transform coding

6. Define is coding redundancy? If the gray level of an image is coded in a way that uses more code words than necessary to

represent each gray level, then the resulting image is said to contain coding redundancy.

7. Define interpixel redundancy? The value of any given pixel can be predicted from the values of its neighbors. The information

carried by is small. Therefore the visual contribution of a single pixel to an image is redundant. Otherwise

called as spatial redundant geometric redundant or

8. What is run length coding? Run-length Encoding, or RLE is a technique used to reduce the size of a repeating string of

characters. This repeating string is called a run; typically RLE encodes a run of symbols into two bytes, a

count and a symbol. RLE can compress any type of data regardless of its information content, but the

content of data to be compressed affects the compression ratio. Compression is normally measured with

the compression ratio:

9. Define compression ratio. Compression Ratio = original size / compressed size: 1

10. Define psycho visual redundancy? In normal visual processing certain information has less importance than other information. So

this information is said to be psycho visual redundant.

11. Define encoder Source encoder is responsible for removing the coding and interpixel redundancy and psycho

visual redundancy.

There are two components A) Source Encoder B) Channel Encoder

CS1002 DIP 2 MARK

12. Define source encoder Source encoder performs three operations

1) Mapper -this transforms the input data into non-visual format. It reduces the interpixel redundancy.

2) Quantizer - It reduces the psycho visual redundancy of the input images .This step is omitted if the

system is error free.

3) Symbol encoder- This reduces the coding redundancy .This is the final stage of encoding process.

13. Define channel encoder The channel encoder reduces reduces the impact of the channel noise by inserting redundant bits

into the source encoded data. Eg: Hamming code

14. What are the types of decoder? Source decoder- has two components

a) Symbol decoder- This performs inverse operation of symbol encoder.

b) Inverse mapping- This performs inverse operation of mapper. Channel decoder-this is omitted if the

system is error free.

15. What are the operations performed by error free compression? 1) Devising an alternative representation of the image in which its interpixel redundant are reduced.

2) Coding the representation to eliminate coding redundancy

16. What is Variable Length Coding? Variable Length Coding is the simplest approach to error free compression. It reduces only the

coding redundancy. It assigns the shortest possible codeword to the most probable gray levels.

17. Define Huffman coding

Huffman coding is a popular technique for removing coding redundancy.

When coding the symbols of an information source the Huffman code yields the smallest possible

number of code words, code symbols per source symbol.

18. Define Block code Each source symbol is mapped into fixed sequence of code symbols or code words. So it is called

as block code.

19. Define instantaneous code A code word that is not a prefix of any other code word is called instantaneous or prefix

codeword.

20. Define uniquely decodable code A code word that is not a combination of any other codeword is said to be uniquely decodable

code.

21. Define B2 code Each code word is made up of continuation bit c and information bit which are binary numbers.

This is called B2 code or B code. This is called B2 code because two information bits are used for

continuation bits

22. Define the procedure for Huffman shift List all the source symbols along with its probabilities in descending order. Divide the total

number of symbols into block of equal size. Sum the probabilities of all the source symbols outside the

reference block.

Now apply the procedure forreference block, including the prefix source symbol. The code words

for the remaining symbols can be constructed by means of one or more prefix code followed by the

reference block as in the case of binary shift code.

23. Define arithmetic coding In arithmetic coding one to one corresponds between source symbols and code word doesn’t exist

where as the single arithmetic code word assigned for a sequence of source symbols. A code word defines

an interval of number between 0 and 1.

CS1002 DIP 2 MARK

24. What is bit plane Decomposition? An effective technique for reducing an image’s interpixel redundancies is to process the image’s

bit plane individually. This technique is based on the concept of decomposing multilevel images into a

series of binary images and compressing each binary image via one of several well-known binary

compression methods.

25. What are three categories of constant area coding? The three categories of constant area coding are

All white

All black

Mixed intensity.

The most probable or frequency occurring is assign a 1 bit code ‘0’, other two categories area assigned as

2 bit code ‘10’ and ‘11’

27. How effectiveness of quantization can be improved?

Introducing an enlarged quantization interval around zero, called a dead zero.

Adapting the size of the quantization intervals from scale to scale. In either case, the selected

quantization intervals must be transmitted to the decoder with the encoded image bit stream.

28. What are the coding systems in JPEG? 1. A lossy baseline coding system, which is based on the DCT and is adequate for most

compression application.

2. An extended coding system for greater compression, higher precision or progressive

reconstruction applications.

3. A lossless independent coding system for reversible compression.

29. What is JPEG? The acronym is expanded as "Joint Photographic Expert Group". It is an international standard in

1992. It perfectly Works with color and grayscale images, Many applications e.g., satellite, medical,...

30. What are the basic steps in JPEG? The Major Steps in JPEG Coding involve:

1. DCT (Discrete Cosine Transformation)

2. Quantization

3. Zigzag Scan_ DPCM on DC component

4. RLE on AC Components

5. Entropy Coding

31. What is MPEG? The acronym is expanded as "Moving Picture Expert Group". It is an international standard in

1992. It perfectly Works with video and also used in teleconferencing Input image Wavelet transform

Quantizer Symbol

encoder

Symbol

decoder

Inverse wavelet

transform

Compressed

image

Compressed image

Decompressed

Image

32. Draw the JPEG Encoder.

33. Draw the JPEG Decoder.

CS1002 DIP 2 MARK

34. What is zig zag sequence? The purpose of the Zig-zag Scan: To group low frequency coefficients in top of vector. Maps 8 x

8 to a 1 x 64 vector

35. Define I-frame I-frame is Intraframe or Independent frame. An I-frame is compressed independently of all

frames. It resembles a JPEG encoded image. It is the reference point for the motion estimation needed to

generate subsequent P and P-frame.

36. Define P-frame P-frame is called predictive frame. A P-frame is the compressed difference between the current

frame and a prediction of it based on the previous I or P-frame

37. Define B-frame B-frame is the bidirectional frame. A B-frame is the compressed difference between the current

frame and a prediction of it based on the previous I or P-frame or next P-frame. Accordingly the decoder

must have access to both past and future reference frames.

UNIT V IMAGE SEGMENTATION AND REPRESENTATION

1. What is segmentation? Segmentation subdivides on image in to its constitute regions or objects. The level to which the

subdivides is carried depends on the problem being solved .That is segmentation should when the objects

of interest in application have been isolated.

2. Write the applications of segmentation.

* Detection of isolated points.

* Detection of lines and edges in an image.

3. What are the three types of discontinuity in digital image? Points, lines and edges.

4. How the derivatives are obtained in edge detection during formulation? The first derivative at any point in an image is obtained by using the magnitude of the gradient at

that point. Similarly the second derivatives are obtained by using the laplacian.

5. Write about linking edge points. The approach for linking edge points is to analyze the characteristics of pixels in a small

neighborhood (3x3 or 5x5) about every point (x,y)in an image that has undergone edge detection. All

points that are similar are linked, forming a boundary of pixels that share some common properties.

6. What are the two properties used for establishing similarity of edge pixels? (1) The strength of the response of the gradient operator used to produce the edge pixel.

(2) The direction of the gradient.

7. What is edge? An edge isa set of connected pixels that lie on the boundary between two regions edges are more

closely modeled as having a ramplike profile. The slope of the ramp is inversely proportional to the

degree of blurring in the edge.

8. Give the properties of the second derivative around an edge? 1. The sign of the second derivative can be used to determine whether an edge pixel lies on the

dark or light side of an edge.

2. It produces two values for every edge in an image.

3. An imaginary straightline joining the extreme positive and negative values of the second

derivative would cross zero near the midpoint of the edge.

9. Define Gradient Operator? First order derivatives of a digital image are based on various approximation of the 2-D gradient.

The gradient of an image f(x,y) at location(x,y) is defined as the vector Magnitude of the vector is

_f=mag( _f )=[Gx2+ Gy2]1/2 (x,y)=tan-1(Gy/Gx) (x,y) is the direction angle of vector _f

CS1002 DIP 2 MARK

10. What is meant by object point and background point? To execute the objects from the background is to select a threshold T that separate these modes.

Then any point (x,y) for which f(x,y)>T is called an object point. Otherwise the point is called

background point.

11. What is global, Local and dynamic or adaptive threshold? When Threshold T depends only on f(x,y) then the threshold is called global . If T depends both

on f(x,y) and p(x,y) is called local. If T depends on the spatial coordinates x and y the threshold is called

dynamic or adaptive where f(x,y) is the original image.

12. Define region growing? Region growing is a procedure that groups pixels or subregions in to layer regions based on

predefined criteria. The basic approach is to start with a set of seed points and from there grow regions by

appending to each seed these neighbouring pixels that have properties similar to the seed.

13. Specify the steps involved in splitting and merging? Split into 4 disjoint quadrants any region Ri for which P(Ri)=FALSE.

Merge any adjacent regions Rj and Rk for which P(RjURk)=TRUE.

Stop when no further merging or splitting is positive.

14. What is meant by markers? An approach used to control over segmentation is based on markers. marker is a connected

component belonging to an image. We have internal markers, associated with objects of interest and

external markers associated with background.

15. What are the 2 principles steps involved in marker selection? The two steps are

1. Preprocessing

2. Definition of a set of criteria that markers must satisfy.

16. Define chain codes?

Chain codes are used to represent a boundary by a connected sequence of straight line segment of

specified length and direction. Typically this representation is based on 4 or 8 connectivity of the

segments . The direction of each segment is coded by using a numbering scheme.

17. What are the demerits of chain code? * The resulting chain code tends to be quite long.

* Any small disturbance along the boundary due to noise cause changes in the code that may not

be related to the shape of the boundary.

18. What is thinning or skeletonizing algorithm? An important approach to represent the structural shape of a plane region is to reduce it to a

graph. This reduction may be accomplished by obtaining the skeletonizing algorithm. It play a central role

in a broad range of problems in image processing, ranging from automated inspection of printed circuit

boards to counting of asbestos fibres in air filter.

19. Specify the various image representation approaches

Chain codes

Polygonal approximation

Boundary segments

20. What is polygonal approximation method ? Polygonal approximation is a image representation approach in which a digital boundary can be

approximated with arbitary accuracy by a polygon.For a closed curve the approximation is exact when the

number of segments in polygon is equal to the number of points in the boundary so that each pair of

adjacent points defines a segment in the polygon.

21. Specify the various polygonal approximation methods

Minimum perimeter polygons

Merging techniques

Splitting techniques

CS1002 DIP 2 MARK

22. Name few boundary descriptors

Simple descriptors

Shape numbers

Fourier descriptors

23. Give the formula for diameter of boundary The diameter of a boundary B is defined as Diam(B)=max[D(pi,pj)] i,j

D-distance measure pi,pj-points on the boundary

24. Define length of a boundary. The length of a boundary is the number of pixels along a boundary.Eg.for a chain coded curve

with unit spacing in both directions the number of vertical and horizontal components plus _2 times the

number of diagonal components gives its exact length.

25. Define eccentricity and curvature of boundary Eccentricity of boundary is the ratio of the major axis to minor axis. Curvature is the rate of

change of slope.

26. Define shape numbers Shape number is defined as the first difference of smallest magnitude. The order n of a shape

number is the number of digits in its representation.

27. Describe Fourier descriptors Fourier descriptor of a boundary can be defined as

K-1

a(u)=1/K_s(k)e-j2_uk/K

k=0

for u=0,1,2……K-1.The complex coefficients a(u) are called Fourier descriptor of a boundary.

The inverse Fourier descriptor is

K-1

s(k)= _ a(u)ej2_uk/K

u=0

for k=0,1,2,……K-1

28. Give the Fourier descriptors for the following transformations (1)Identity (2)Rotation (3)Translation (4)Scaling (5)Starting point

29. Specify the types of regional descriptors

Simple descriptors

Texture

30. Name few measures used as simple descriptors in region descriptors

Area

Perimeter

Compactness

Mean and median of gray levels

Minimum and maximum of gray levels

Number of pixels with values above and below mean

31. Define compactness Compactness of a region is defined as (perimeter)^2/area.It is a dimensionless quantity and is

insensitive to uniform scale changes.

32. Describe texture Texture is one of the regional descriptors. It provides measures of properties such as smoothness,

coarseness and regularity. There are 3 approaches used to describe texture of a region.

They are:

Statistical

Structural

Spectral

CS1002 DIP 2 MARK

33. Describe statistical approach Statistical approaches describe smooth,coarse,grainy characteristics of texture.This is the simplest

one compared to others.It describes texture using statistical moments of the gray-level histogram of an

image or region.

34. Define gray-level co-occurrence matrix. A matrix C is formed by dividing every element of A by n(A is a k x k matrix and n is the total

number of point pairs in the image satisfying P(position operator). The matrix C is called gray-level co-

occurrence matrix if C depends on P,the presence of given texture patterns may be detected by choosing

an appropriate position operator.

35. Explain structural and spectral approach Structural approach deals with the arrangement of image primitives such as description of texture

based on regularly spaced parallel lines.

Spectral approach is based on properties of the Fourier spectrum and are primarily to detect

global periodicity in an image by identifying high energy, narrow peaks in spectrum.There are 3 features

of Fourier spectrum that are useful for texture description.

They are:

Prominent peaks in spectrum gives the principal direction of texture patterns.

The location of peaks in frequency plane gives fundamental spatial period of patterns.

Eliminating any periodic components by our filtering leaves non- periodic image elements.

16 MARKS

UNIT I

1. Explain the steps involved in digital image processing.

(or)

Explain various functional block of digital image processing # Image acquisition

# Preprocessing

# Segmentation

# Representation and Description

# Recognition and Interpretation

2. Describe the elements of visual perception. # Cornea and Sclera

# Choroid – Iris diaphragm and Ciliary body

# Retina- Cones and Rods

3. Describe image formation in the eye with brightness adaptation and discrimination # Brightness adaptation

# Subjective brightness

# Weber ratio

#Mach band effect

#simultaneous contrast

4. Write short notes on sampling and quantization. # Sampling

# Quantization

# Representing Digital Images

5. Describe the functions of elements of digital image processing system with a diagram. # Acquisition

# Storage

# Processing

# Communication

# Display

CS1002 DIP 2 MARK

6. Explain the basic relationships between pixels? # Neighbors of a pixel

# Connectivity, Adjacency, Path

# Distance Measure

# Arithmetic and Logic Operations

7. Explain the properties of 2D Fourier Transform. # Separability

# Translation

# Periodicity and Conjugate Symmetry

# Rotation

# Distribution and Scaling

# Average Value

# Laplacian

# Convolution and correlation

# Sampling

8. ( i )Explain convolution property in 2D fourier transform. * 1D Continuous

* 1D Discrete

* 1D convolution theorem

* 2D continuous

* 2D Discrete

* 2D convolution theorem

(ii) Find F (u) and |F (u)|

9. Explain Fast Fourier Transform (FFT) in detail. # FFT Algorithm

# FFT Implementation

10. Explain in detail the different separable transforms # Forward 1D DFT & 2D DFT

# Inverse 1D DFT & 2D DFT

# Properties

11. Explain Hadamard transformation in detail. # 1D DHT

# 1D Inverse DHT

# 2D DHT

# 2D Inverse DHT

12. Discuss the properties and applications of

1)Hadamard transform 2)Hotelling transform # Properties of hadamard:

Real and orthogonal

fast transform

faster than sine transform

Good energy compaction for image

# Appl:

Image data compression, filtering and design of course

# Properties of hotelling:

Real and orthogonal

Not a fast transform

Best energy compaction for image

# Appl:

Useful in performance evaluation & for finding performance

Bounds

CS1002 DIP 2 MARK

13. Explain Haar transform in detail. # Def P= 2P+q-1

# Find h k (z)

14. Explain K-L transform in detail.

UNIT II

1. Explain the types of gray level transformation used for image enhancement. # Linear (Negative and Identity)

# Logarithmic( Log and Inverse Log)

# Power_law (nth root and nth power)

# Piecewise_linear (Constrast Stretching, Gray level Slicing, Bit plane Slicing)

2. What is histogram? Explain histogram equalization. # P(rk) = nk/n

# Ps(s) = 1 means histogram is arranged uniformly.

3. Discuss the image smoothing filter with its model in the spatial domain. # LPF-blurringMedian filter – noise reduction & for sharpening image

4. What are image sharpening filters. Explain the various types of it. # used for highlighting fine details

# HPF-output gets sharpen and background becomes darker

# High boost- output gets sharpen but background remains unchanged

# Derivative- First and Second order derivatives

Appl: # Medical image

# electronic printing

# industrial inspection

5. Explain spatial filtering in image enhancement. # Basics

# Smoothing filters

# Sharpening filters

6. Explain image enhancement in the frequency domain. # Smoothing filters

# Sharpening filters

# Homomorphic filtering

7. Explain Homomorphic filtering in detail. # f(x, y) = i(x, y) . r(x, y)

# Calculate the enhanced image g(x,y)

UNIT III

1. Explain the algebra approach in image restoration. # Unconstrained

# Constrained

2. What is the use of wiener filter in image restoration. Explain. # Calculate f^

# Calculate F^(u, v)

3. What is meant by Inverse filtering? Explain. # Recovering i/p from its o/p

# Calculate f^(x, y)

4. Explain singular value decomposition and specify its properties. # U= m=1_r___m _m T

This equation is called as singular value decomposition of an image.

CS1002 DIP 2 MARK

# Properties

The SVD transform varies drastically from image to image.

The SVD transform gives best energy packing efficiency for any givenimage.

The SVD transform is useful in the design of filters finding least

square,minimum solution of linear equation and finding rank of large matrices.

5. Explain image degradation model /restoration process in detail. # Image degradation model /restoration process diagram

# Degradation model for Continuous function

# Degradation model for Discrete function – 1_D and 2_D

6. What are the two approaches for blind image restoration? Explain in detail. _ Direct measurement

_ Indirect estimation

UNIT IV

1. What is data redundancy? Explain three basic data redundancy? Definition of data redundancy

The 3 basic data redundancy are

_ Coding redundancy

_ Interpixel redundancy

_ Psycho visual redundancy

2. What is image compression? Explain any four variable length coding

compression schemes. Definition of image compression

Variable Length Coding

* Huffman coding

* B2 Code

* Huffman shift

* Huffman Truncated

* Binary Shift

*Arithmetic coding

3. Explain about Image compression model? The source Encoder and Decoder

The channel Encoder and Decoder

4. Explain about Error free Compression? a. Variable Length coding

i. Huffman coding

ii. Arithmetic coding

b. LZW coding

c. Bit Plane coding

d. Lossless Predictive coding

5. Explain about Lossy compression? Lossy predictive codingTransform coding

Wavelet coding

6. Explain the schematics of image compression standard JPEG. Lossy baseline coding system

Extended coding system

Lossless Independent coding system

7. Explain how compression is achieved in transform coding and explain about DCT _ Block diagram of encoder

CS1002 DIP 2 MARK

_ decoder

_ Bit allocation

_ 1D transform coding

_ 2D transform coding, application

_ 1D,2D DCT

8. Explain arithmetic coding _ Non-block code

_ One example

9. Explain about Image compression standards? _ Binary Image compression standards

_ Continuous tone still Image compression standards

_ Video compression standards

10. Discuss about MPEG standard and compare with JPEG _ Motion Picture Experts Group

1. MPEG-1

2. MPEG-2

3. MPEG-4

_ Block diagram

_ I-frame

_ p-frame

_ B-frame

UNIT V 1. What is image segmentation. Explain in detail.

Definition - image segmentation

Discontinity – Point, Line, Edge

Similarity – Thresholding, Region Growing, Splitting and Merging

2. Explain Edge Detection in details? * Basic formation.

* Gradient Operators

* Laplacian Operators

3. Define Thresholding and explain the various methods of thresholding in detail? Foundation

The role of illumination

Basic adaptive thresholding

Basic adaptive thresholding

Optimal global & adaptive thresholding.

4. Discuss about region based image segmentation techniques. Compare threshold region based

techniques. * Region Growing

* Region splitting and merging

* Comparison

5. Define and explain the various representation approaches?

chain codes

Polygon approximations

Signature

Boundary segments

CS1002 DIP 2 MARK

Skeletons.

6. Explain Boundary descriptors.

Simple descriptors.

Fourier descriptors.

7. Explain regional descriptors Simple descriptors

Texture

i. Statistical approach

ii. Structural approach

iii. Spectral approach

8. Explain the two techniques of region representation. _ Chain codes

_ Polygonol approximation

9. Explain the segmentation techniques that are based on finding the regions

directly. _ Edge detection line detection

_ Region growing

_ Region splitting

_ region merging

10. How is line detected? Explain through the operators Types of line masks1. horizontal

2. vertical

3. +45°,-45°

CS1002 DIP 2 MARK

ST. ANNE’S COLLEGE OF ENNGINEERING & TECHNOLOGY

ANGUCHETTYPALAYAM, PANRUTI-607 110 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

CS1002 DIGITAL IMAGE PROCESSING

YEAR : IV SEM:VIII

IMPORTANT 2 MARK QUESTIONS 1. Define Image?

An Image may be defined as a two dimensional function f(x,y) where x & y are spatial (plane)

coordinates, and the amplitude of f at any pair of coordinates (x,y) is called intensity or gray level of the

image at that point. When x,y and the amplitude values of f are all finite, discrete quantities we call the

image as Digital Image.

2. Define Image Sampling?

Digitization of spatial coordinates (x,y) is called Image Sampling. To be suitable for computer

processing, an image function f(x,y) must be digitized both spatially and in magnitude.

3. Define Quantization?

Digitizing the amplitude values is called Quantization. Quality of digital image is determined to a

large degree by the number of samples and discrete gray levels used in sampling and quantization.

4. What is Dynamic Range?

The range of values spanned by the gray scale is called dynamic range of an image. Image will

have high contrast, if the dynamic range is high and image will have dull washed out gray look if the

dynamic range is low.

5. Define Mach band effect?

The spatial interaction of Luminance from an object and its surround creates a phenomenon

called the mach band effect.

6. Define Brightness?

Brightness of an object is the perceived luminance of the surround. Two objects with different

surroundings would have identical luminance but different brightness.

7. Define Tapered Quantization?

If gray levels in a certain range occur frequently while others occurs rarely, the quantization

levels are finely spaced in this range and coarsely spaced outside of it. This method is sometimes called

Tapered Quantization.

8. What do you meant by Gray level?

Gray level refers to a scalar measure of intensity that ranges from black to grays

and finally to white.

9. What do you meant by Color model?

A Color model is a specification of 3D-coordinates system and a subspace within that system

where each color is represented by a single point.

10. List the hardware oriented color models?

1. RGB model

2. CMY model

3. YIQ model

4. HSI model

11. What is Hue of saturation?

Hue is a color attribute that describes a pure color where saturation gives a measure of the degree

to which a pure color is diluted by white light.

12. List the applications of color models?

1. RGB model--- used for color monitor & color video camera

2. CMY model---used for color printing

3. HIS model----used for color image processing

4. YIQ model---used for color picture transmission

CS1002 DIP 2 MARK

13. What is Chromatic Adoption?

` The hue of a perceived color depends on the adoption of the viewer. For example, the American

Flag will not immediately appear red, white, and blue of the viewer has been subjected to high intensity

red light before viewing the flag. The color of the flag will appear to shift in hue toward the red

component cyan.

14. Define Resolutions?

Resolution is defined as the smallest number of discernible detail in an image. Spatial resolution

is the smallest discernible detail in an image and gray level resolution refers to the smallest discernible

change is gray level.

15. Write the M X N digital image in compact matrix form?

16. Write the expression to find the number of bits to store a digital image?

The number of bits required to store a digital image is b=M X N X k

When M=N, this equation becomes b=N^2k

17. What do you meant by Zooming of digital images?

Zooming may be viewed as over sampling. It involves the creation of new pixel locations and the

assignment of gray levels to those new locations.

18. What do you meant by shrinking of digital images?

Shrinking may be viewed as under sampling. To shrink an image by one half, we delete every

row and column. To reduce possible aliasing effect, it is a good idea to blue an image slightly before

shrinking it.

19. Define the term Radiance?

Radiance is the total amount of energy that flows from the light source, and it is usually measured

in watts (w).

20. Define the term Luminance?

Luminance measured in lumens (lm), gives a measure of the amount of energy an observer

perceiver from a light source.

21. What is Image Transform?

An image can be expanded in terms of a discrete set of basis arrays called basis images. These

basis images can be generated by unitary matrices. Alternatively, a given NXN image can be viewed as

an N^2X1 vectors. An image transform provides a set of coordinates or basis vectors for vector space.

22. What are the applications of transform.

1) To reduce band width

2) To reduce redundancy

3) To extract feature.

23. Give the Conditions for perfect transform?

Transpose of matrix = Inverse of a matrix. Orthoganality.

24. What are the properties of unitary transform?

1) Determinant and the Eigen values of a unitary matrix have unity magnitude

2) the entropy of a random vector is preserved under a unitary Transformation

3) Since the entropy is a measure of average information, this means information is preserved under a

unitary transformation.

CS1002 DIP 2 MARK

25. Write the expression of one-dimensional discrete Fourier transforms

Forward transform

The sequence of x(n) is given by x(n) = { x0,x1,x2,… xN-1}.

X(k) = (n=0 to N-1) ∑ x(n) exp(-j 2* pi* nk/N) ; k= 0,1,2,…N-1

Reverse transforms

X(n) = (1/N) (k=0 to N-1) ∑x(k) exp(-j 2* pi* nk/N) ; n= 0,1,2,…N-1

26. Properties of twiddle factor.

1. Periodicity WN^(K+N)= WN^K

2. Symmetry WN^(K+N/2)= -WN^K

27. Give the Properties of one-dimensional DFT

1. The DFT and unitary DFT matrices are symmetric.

2. The extensions of the DFT and unitary DFT of a sequence and their inverse transforms are periodic

with period N.

3. The DFT or unitary DFT of a real sequence is conjugate symmetric about N/2.

28. Give the Properties of two-dimensional DFT

1. Symmetric

2. Periodic extensions

3. Sampled Fourier transform

4. Conjugate symmetry.

29. What is cosine transform?

The NXN cosine transform c(k) is called the discrete cosine transform and is defined as

C(k) = 1/√N , k=0, 0 ≤ n ≤N-1

= √ (2/N) cos (pi (2n+1)/2N 1≤k ≤ N-1, 0≤n ≤ N-1

30. What is sine transform?

The NXN sine transform matrix Ψ = Ψ (k,n) also called the discrete sine transform , is

defined as

Ψ (k,n) = √ (2/N+1) sin [ pi* (k+1) (n+1) / (N+1)] , 0≤k, n≤N-1

31. Write the properties of cosine transform:

1) Real & orthogonal.

2) Fast transform.

3) Has excellent energy compaction for highly correlated data

32. Write the properties of sine transform:

1) Real, symmetric and orthogonal.

2) Not the imaginary part of the unitary DFT.

3) Fast transform.

33. Write the properties of Hadamard transform

1) Hadamard transform contains any one value.

2) No multiplications are required in the transform calculations.

3) The no: of additions or subtractions required can be reduced from N^2 to about Nlog2N

4) Very good energy compaction for highly correlated images.

34 Define Haar transform:

The Haar functions are defined on a continuous interval Xe [0,1] and for K=0,1, N-1 where

N=2^n.. The integer k can be uniquely decomposed as K=2^P+Q-1.

35. Write the expression for Hadamard transforms

Hadamard transform matrices Hn are NXN matrices where N=2^n , n= 1,2,3,… is

defined as Hn= Hn-1 * H1 = H1* Hn-1

36. What are the properties of Haar transform.

1. Haar transform is real and orthogonal.

2. Haar transform is a very fast transform

3. Haar transform has very poor energy compaction for images

CS1002 DIP 2 MARK

4. The basic vectors of Haar matrix sequensly ordered.

37. What are the Properties of Slant transform

1. Slant transform is real and orthogonal.

2. Slant transform is a fast transform

3. Slant transform has very good energy compaction for images

4. The basic vectors of Slant matrix are not sequensely ordered.

38. Define of KL Transform

KL Transform is an optimal in the sense that it minimizes the mean square error between the

vectors X and their approximations X^. Due to this idea of using the Eigen vectors corresponding to

largest Eigen values. It is also known as principal component transform.

39. Justify that KLT is an optimal transform.

Since mean square error of reconstructed image and original image is minimum and the mean

value of transformed image is zero so that uncorrelated.

40. What is Image Enhancement?

Image enhancement is to process an image so that the output is more suitable for specific

application.

41. Name the categories of Image Enhancement and explain?

The categories of Image Enhancement are

1. Spatial domain

2. Frequency domain

Spatial domain: It refers to the image plane, itself and it is based on direct manipulation of pixels of an

image.

Frequency domain techniques are based on modifying the Fourier transform of an image.

42. What do you mean by Point processing?

Image enhancement at any Point in an image depends only on the gray level at that point is often

referred to as Point processing.

43. Explain Mask or Kernels?

A Mask is a small two-dimensional array, in which the value of the mask

coefficient determines the nature of the process, such as image sharpening.

44. What is Image Negatives?

The negative of an image with gray levels in the range [0, L-1] is obtained by using the negative

transformation, which is given by the expression. s = L-1-r Where s is output pixel,r is input pixel

456. Define Histogram?

The histogram of a digital image with gray levels in the range [0, L-1] is a discrete function

h (rk) = nk, where rk is the kth gray level and nk is the number of pixels in the image having gray level

rk.

46. Define Derivative filter?

For a function f (x, y), the gradient f at co-ordinate (x, y) is defined as the vector

47. Explain spatial filtering?

Spatial filtering is the process of moving the filter mask from point to point in an image. For

linear spatial filter, the response is given by a sum of products of the filter coefficients, and the

corresponding image pixels in the area spanned by the filter mask.

48. Define averaging filters?

The output of a smoothing, linear spatial filter is the average of the pixels contained in the

neighborhood of the filter mask. These filters are called averaging filters.

CS1002 DIP 2 MARK

49. What is a Median filter?

The median filter replaces the value of a pixel by the median of the gray levels in the

neighborhood of that pixel.

50. What is maximum filter and minimum filter?

The 100th percentile is maximum filter is used in finding brightest points in an image. The 0th

percentile filter is minimum filter used for finding darkest points in an image.

51. Define high boost filter?

High boost filtered image is defined as HBF= A (original image)-LPF

= (A-1) original image + original image –LPF

HBF= (A-1) original image +HPF

52. State the condition of transformation function s=T(r)

1. T(r) is single-valued and monotonically increasing in the interval 0_r_1 and

2. 0_T(r) _1 for 0_r_1.

53. Write the application of sharpening filters?

1. Electronic printing and medical imaging to industrial application

2. Autonomous target detection in smart weapons.

54. Name the different types of derivative filters?

1. Perwitt operators

2. Roberts cross gradient operators

3. Sobel operators.

55. Define Restoration?

Restoration is a process of reconstructing or recovering an image that has been degraded by using

a priori knowledge of the degradation phenomenon. Thus restoration techniques are oriented towards

modeling the degradation and applying the inverse process in order to recover the original image.

56. How a degradation process id modeled?

A system operator H, which together with an additive white noise term _(x,y) a operates on an

input image f(x,y) to produce a degraded image g(x,y).

57. What is homogeneity property and what is the significance of this property?

H [k1f1(x,y)] = k1H[f1(x,y)]

Where H=operator

K1=constant

f(x,y)=input image.

It says that the response to a constant multiple of any input is equal to the response to that input

multiplied by the same constant.

58. What is fredholm integral of first kind?

which is called the superposition or convolution or fredholm integral of first kind. It states

that if the response of H to an impulse is known, the response to any input f(α,β) can be

calculated by means of fredholm integral.

CS1002 DIP 2 MARK

59. Define circulant matrix?

A square matrix, in which each row is a circular shift of the preceding row and thefirst row is a

circular shift of the last row, is called circulant matrix.Example:

60. What is the concept behind algebraic approach to restoration? Algebraic approach is the concept of seeking an estimate of f, denoted f^, that minimizes a

prefined criterion of performance where f is the image.

61. Why the image is subjected to wiener filtering?

This method of filtering consider images and noise as random process and the objective is to find

an estimate f^ of the uncorrupted image f such that the mean square error between them is minimized. So

that image is subjected to wiener filtering to minimize the error.

62. Define spatial transformation?

Spatial transformation is defined as the rearrangement of pixels on an image plane.

63. Define Gray-level interpolation?

Gray-level interpolation deals with the assignment of gray levels to pixels in the spatially

transformed image.

64. Give one example for the principal source of noise?

The principal source of noise in digital images arise image acquisition (digitization) and/or

transmission. The performance of imaging sensors is affected by a variety of factors, such as

environmental conditions during image acquisition and by the quality of the sensing elements. The factors

are light levels and sensor temperature.

65. When does the degradation model satisfy position invariant property?

An operator having input-output relationship g(x,y)=H[f(x,y)] is said to position invariant if

H[f(x- α,y- β _)]=g(x- α,y- β) for any f(x,y) and α and β.

This definition indicates that the response at any point in the image depends only on the value of

the input at that point not on its position.

66. Why the restoration is called as unconstrained restoration?

In the absence of any knowledge about the noise ‘n’, a meaningful criterion function is to seek an

f^ such that H f^ approximates of in a least square sense by assuming the noise term is as small as

possible.

Where H = system operator.

f^ = estimated input image.

g = degraded image.

67. Which is the most frequent method to overcome the difficulty to formulate the spatial relocation of

pixels?

The point is the most frequent method, which are subsets of pixels whose location in the input

(distorted) and output (corrected) imaged is known precisely.

68. What are the three methods of estimating the degradation function?

1. Observation

2. Experimentation 3. Mathematical modeling.

69. How the blur is removed caused by uniform linear motion?

An image f(x,y) undergoes planar motion in the x and y-direction and x0(t) and y0(t) are the time

varying components of motion. The total exposure at any point of the recording medium (digital memory)

is obtained by integrating the instantaneous exposure over the time interval during which the imaging

system shutter is open.

CS1002 DIP 2 MARK

70. What is inverse filtering?

The simplest approach to restoration is direct inverse filtering, an estimate F^(u,v) of the

transform of the original image simply by dividing the transform of the degraded image G^(u,v) by the

degradation function. F^ (u,v) = G^(u,v)/H(u,v)

71 Give the difference between Enhancement and Restoration?

Enhancement technique is based primarily on the pleasing aspects it might present to the viewer.

For example: Contrast Stretching. Where as Removal of image blur by applying a deblurrings function is

considered a restoration technique.

72. What is segmentation?

The first step in image analysis is to segment the image. Segmentation subdivides an image into

its constituent parts or objects.

73. Write the applications of segmentation.

(i) Detection of isolated points.

(ii) Detection of lines and edges in an image.

74. What are the three types of discontinuity in digital image?

Points, lines and edges.

75. How the discontinuity is detected in an image using segmentation?

(i) Compute the sum of the products of the coefficient with the gray levels contained in the region

encompassed by the mask.

(ii) The response of the mask at any point in the image is R = w1z1+ w2z2 + w3z3 +………..+ w9z9

Where zi = gray level of pixels associated with mass coefficient wi.

(iii) The response of the mask is defined with respect to its center location.

76. Why edge detection is most common approach for detecting discontinuities?

The isolated points and thin lines are not frequent occurrences in most practical applications, so

edge detection is mostly preferred in detection of discontinuities.

77, How the derivatives are obtained in edge detection during formulation?

The first derivative at any point in an image is obtained by using the magnitude of the gradient at

that point. Similarly the second derivatives are obtained by using the laplacian.

78. Write about linking edge points.

The approach for linking edge points is to analyse the characteristics of pixels in a small

neighborhood (3x3 or 5x5) about every point (x,y)in an image that has undergone edge detection. All

points that are similar are linked, forming a boundary of pixels that share some common properties.

79. What are the two properties used for establishing similarity of edge pixels?

(1) The strength of the response of the gradient operator used to produce the edge

pixel.

(2) The direction of the gradient.

80. Explain about gradient operator.

The gradient of an image f(x,y) at location (x,y) is the vector

The gradient vector points are in the direction of maximum rate of change of f at (x,y)

In edge detection an important quantity is the magnitude of this vector (gradient) and is denoted as

The direction of gradient vector also is an important quantity.

CS1002 DIP 2 MARK

81. What is the advantage of using sobel operator?

Sobel operators have the advantage of providing both the differencing and a smoothing effect.

Because derivatives enhance noise, the smoothing effect is particularly attractive feature of the sobel

operators.

82. What is pattern?

Pattern is a quantitative or structural description of an object or some other entity of interest in an

image. It is formed by one or more descriptors.

83. What is pattern class?

It is a family of patterns that share some common properties. Pattern classes are denoted as w1

w2 w3 ……… wM , where M is the number of classes.

84. What is pattern recognition?

It involves the techniques for arranging pattern to their respective classes by automatically and

with a little human intervention.

85. What are the three principle pattern arrangements?

The three principal pattern arrangements are vectors, Strings and trees. Pattern vectors are

represented by old lowercase letters such as x y z and in In the form x=[x1, x2, ……….., xn ] Each

component x represents I th descriptor and n is the number of such descriptor.

86. What is Data Compression?

Data compression requires the identification and extraction of source redundancy. In other words,

data compression seeks to reduce the number of bits used to store or transmit information.

87. What are two main types of Data compression?

• Lossless compression can recover the exact original data after compression. It is used mainly for

compressing database records, spreadsheets or word processing files, where exact replication of the

original is essential.

• Lossy compression will result in a certain loss of accuracy in exchange for a substantial increase in

compression. Lossy compression is more effective when used to compress graphic images and digitised

voice where losses outside visual or aural perception can be tolerated.

88. What is the Need For Compression?

In terms of storage, the capacity of a storage device can be effectively increased with methods

that compress a body of data on its way to a storage device and decompresses it when it is retrieved. In

terms of communications, the bandwidth of a digital communication link can be effectively increased by

compressing data at the sending end and decompressing data at the receiving end.

At any given time, the ability of the Internet to transfer data is fixed. Thus, if data can effectively

be compressed wherever possible, significant improvements of data throughput can be achieved. Many

files can be combined into one compressed document making sending easier.

89. What are different Compression Methods?

Run Length Encoding (RLE)

Arithmetic coding

Huffman coding and

Transform coding

90. What is run length coding?

Run-length Encoding, or RLE is a technique used to reduce the size of a repeating string of

characters. This repeating string is called a run; typically RLE encodes a run of symbols into two bytes, a

count and a symbol. RLE can compress any type of data regardless of its information content, but the

content of data to be compressed affects the compression ratio. Compression is normally measured with

the compression ratio:

91. Define compression ratio.

Compression Ratio = original size / compressed size: 1

92. Give an example for Run length Encoding.

Consider a character run of 15 'A' characters, which normally would require 15 bytes

to store: AAAAAAAAAAAAAAA coded into 15A

CS1002 DIP 2 MARK

With RLE, this would only require two bytes to store; the count (15) is stored as the first byte and the

symbol (A) as the second byte.

93. What is Huffman Coding?

Huffman compression reduces the average code length used to represent the symbols of an

alphabet. Symbols of the source alphabet, which occur frequently, are assigned with short length codes.

The general strategy is to allow the code length to vary from character to character and to ensure that the

frequently occurring characters have shorter codes.

94. What is Arithmetic Coding?

Arithmetic compression is an alternative to Huffman compression; it enables characters to be

represented as fractional bit lengths. Arithmetic coding works by representing a number by an interval of

real numbers greater or equal to zero, but less than one. As a message becomes longer, the interval needed

to represent it becomes smaller and smaller, and the number of bits needed to specify it increases.

95. What is JPEG?

The acronym is expanded as "Joint Photographic Expert Group". It is an international standard in

1992. It perfectly Works with colour and greyscale images, Many applications e.g., satellite, medical,...

96. What are the basic steps in JPEG?

The Major Steps in JPEG Coding involve:

_ DCT (Discrete Cosine Transformation)

_ Quantization

_ Zigzag Scan

_ DPCM on DC component

_ RLE on AC Components

_ Entropy Coding

97. What is MPEG?

The acronym is expanded as "Moving Picture Expert Group". It is an international standard in

1992. It perfectly Works with video and also used in teleconferencing

98. What is transform coding?

Transform coding is used to convert spatial image pixel values to transform coefficient values.

Since this is a linear process and no information is lost, the number of coefficients produced is equal to

the number of pixels transformed.

The desired effect is that most of the energy in the image will be contained in a few large

transform coefficients. If it is generally the same few coefficients that contain most of the energy in most

pictures, then the coefficients may be further coded by loss less entropy coding. In addition, it is likely

that the smaller coefficients can be coarsely quantized or deleted (lossy coding) without doing visible

damage to the reproduced image.

99. What are the different transforms used in transform coding and how the differ?

Many types of transforms used for picture coding, are Fourier, Karhonen-Loeve, Walsh-

Hadamard, lapped orthogonal, discrete cosine (DCT), and recently, wavelets. The various transforms

differ among themselves in three basic ways that are of interest in picture coding:

1) The degree of concentration of energy in a few coefficients;

2) The region of influence of each coefficient in the reconstructed picture;

3) The appearance and visibility of coding noise due to coarse quantization of the coefficients.

100. Draw the JPEG Encoder.

CS1002 DIP 2 MARK

101. Draw the JPEG Decoder.

102. What is zig zag sequence?

The purpose of the Zig-zag Scan:

To group low frequency coefficients in top of vector.

Maps 8 x 8 to a 1 x 64 vector

103.Explain the term digital image.

The digital image is an array of real or complex numbers that is represented by a finite no of bits.

104.Write any four applications of DIP.

i. Remote sensing

ii. Image transmission and storage for business application

iii. Medical imaging

iv. Astronomy

105.What is the effect of Mach band pattern.

The intensity or the brightness pattern perceive a darker stribe in region D and brighter stribe in

region B.This effect is called Mach band pattern or effect.

106.Find the number of bits to store a 128_128 image with 64 gray levels.

Given:

M = N = 128 L = 64 =2k

=> k=6 No. of bits = M2k

= 1282*6

= 98304 bits

107.Name the types of connectivity and explain

a. 4-connectivity:

Two pixels p and q with values from V are 4-connected if q is in the set N4(p)

b. 8- connectivity:

Two pixels p and q with values from V are 8-connected if q is in the set N8(p)

c. m- connectivity:

Two pixels p and q with values from V are m-connected if

i. q is in N4(p) or

ii. q is in ND(p) and the set N4(p) ñ N4(q) = Φ

108.Define the chessboard distance

It is also known as D8 distance given by D8 (p,q) = max( x-s , y-t )

The pixels with D8 distance from (x,y) less than or equal to some value r form a square centered

at (x,y).

CS1002 DIP 2 MARK

109.Write down the properties of 2D fourier transform.

• Separability

• Translation

• Periodicity and Conjugate property

• Rotation

• Distributivity and scaling

• Average value

• Convolution and Correlation

• Laplacian

110.Obtain the Hadamard transformation for N = 4

N = 4 = 2n => n = 2

111.Write down the properties of Haar transform.

• Real and orthogonal

• Very fast transform

• Basis vectors are sequentially ordered

• Has fair energy compaction for image

• Useful in feature extraction,image coding and image analysis problem

112.What is enhancement.

Image enhancement is a technique to process an image so that the result is more suitable than the

original image for specific applications;

113.What is point processing.

Enhancement at any point in an image depends only on the gray level at that point is referred to

as point processing.

114.What is gray level slicing.

Highlighting a specific range of gray levels in an image is referred to as gray level slicing.It is

used in satellite imagery and x-ray images

115.What is histogram equalization

It is a technique used to obtain linear histogram . It is also known as histogram linearization.

Condition for uniform histogram is Ps(s) = 1

116.Discuss the Roberts cross-gradient operators.

Using cross difference ∆f = |z5-z9|+|z6-z8| ∆f can be implemented by 2x 2 mask

This is called as Roberts cross gradient operator

117.Define the degradation phenomena.

Image restoration or degradation is a process that attempts to reconstruct or recover an image that

has been degraded by using some clear knowledge of the degradation phenomena. Degradation may be in

the form of

• Sensor noise

• Blur due to camera misfocus

• Relative object camera motion

118.What is unconstrained restoration.

It is also known as least square error approach. n = g-Hf

To estimate the original image f^, noise n has to be minimized and f^ = g/H

CS1002 DIP 2 MARK

119.Draw the image observation model.

120.What is blind image restoration

Degradation may be difficult to measure or may be time varying in an unpredictable manner. In

such cases information about the degradation must be extracted from the observed image either explicitly

or implicitly. This task is called blind image restoration.

16 marks 1.Explain various functional block of digital image processing

Hint:

Block diagram and explain about each blocks (Image acquisition, enhancement, restoration, color

image, wavelets, compression, segmentation, representation & description, recognition).

2.Briefly explain the elements of human visual system

Hint:

Diagram-Structure of human eye

Three membranes-cornea,sclera,choroids;Lens;Retina-Cones,rods;Blind spot;Fovea

3.Describe image formation in the eye with brightness adaptation and discrimination

Hint:

Brightness adaptation-large variation of intensity by changes in its overall sensitivity.Subjective

brightness,Weber ratio,Mach band effect ,simultaneous contrast

4.Explain in detail the different separable transforms

Hint:

Forward 1D DFT & 2D DFT, Inverse 1D DFT & 2D DFT

Properties

5.Discuss the DFT and FFT

Hint:

DFT:1D FT & its inverse,2D DFT & its inverse,propertiesseparability,

Translation,Periodicity and Conjugate property ,Rotation,Distributivity and

scaling,Average value,Convolution and Correlation,Laplacian

FFT: FFT algorithm-successive doubling method & inverse FFT & no. of operations

FFT implementation-bit reversal format

6.Explain in detail about the KL transform with examples

Hint:

Also known as hotelling transform

7.Explain the Hadamard transform matrices Hn and also its properties

8.Explain DCT and its properties

Hint:

Forward 1D DCT & 2D DCT, Inverse 1D DCT & 2D DCT

Properties

9.Define Haar transform.Derive the same for n=8.What are its properties

Hint:

Based on haar function hk (z) defined over z_ (0, 1) K=2p+q-1 And properties

CS1002 DIP 2 MARK

10.Discuss the properties and applications of 1)Hadamard transform 2)Hotelling transform

Hint:

Properties of hadamard:

Real and orthogonal

fast transform

faster than sine transform

Good energy compaction for image

Appl:

Image data compression, filtering and design of course

Properties of hotelling:

Real and orthogonal

Not a fast transform

Best energy compaction for image

Appl:

Useful in performance evaluation & for finding performance bounds

11.Discuss the image smoothing filter with its model in the spatial domain.

Hint:

LPF-blurring

Median filter – noise reduction & for sharpening image

12.What are image sharpening filters.Explain the various types of it.

Hint:

used for highlighting fine details

HPF-output gets sharpen and background becomes darker

High boost- output gets sharpen but background remains unchanged

Appl:

Medical image,electronic printing,industrial inspection

13.Discuss in detail about homomorphic & derivative filters.

Hint:

Homomorphic:Improving the appearance of an image by simultaneous compression and

contrast enhancement. f(x,y)= i(x,y)r(x,y)

block diagram Derivative:To obtain more sharpened image Roberts cross gradient operator ,prewitt

operator, sobel operator

14.Explain Weiner smoothing filter and its relation with inverse filtering and diffracted

limited systems.

Hint:

Weiner filter: Mean square errore

Inverse filter:

H-1(w1, w2) = 1/H (w1, w2)

15.Explain the Constrained least square filtering.

Hint:

f^=gHT/(HHT+_QQT)

16.Discuss in detail about Constrained and Unconstrained filters

Hint:

Unconstrained: f^ = g/H

constrained: f^=gHT/(HHT+_QQT)

17.Write notes on 1)Pseudo inverse filter 2)SVD

18.Explain Huffman coding algorithm giving a numerical example.

Used for text compression

Calculate Lavg,entropy, efficiency,redundancy,variance Minimum variance method,advantages

CS1002 DIP 2 MARK

19.Explain the types of error free compression technique.

Hint:

Variable length coding

LZW coding

Bit plane coding

20.Explain how compression is achieved in transform coding and explain about DCT

Hint:

Block diagram of encoder,decoder ,Bit allocation, 1D transform coding, 2D transform

coding,application and explain 1D,2D DCT

21.Explain arithmetic coding

Hint:

Non-block code

Explain with one example

22.Explain various functional block of JPEG standard.

Hint:

Compression standard, 2 modes, 3 different coding system-lossy baseline,extended,

lossless independent. JPEG baseline coding and decoding.

DC coefficient is

23.Discuss about MPEG standard and compare with JPEG

Hint:

Motion Picture Experts Group-MPEG-1,MPEG-2,MPEG-4.block diagram. I-frame, pframe,

B-frame

24.Explain the two techniques of region representation.

Hint:

Chain codes,Polygonol approximation

25.Explain the segmentation techniques that are based on finding the regions directly.

Hint:

Edge detection, line detection, Region growing, Region splitting, region merging

26.How is line detected. Explain through the operators

Hint:

Explain with various types of line masks-horizontal,vertical,+45°,-45°

CS1002 DIP 2 MARK

16- Marks Questions

1. Explain the Hadamard transform matrices Hn and also its properties?

2. Explain DCT and its properties?

3. Explain various functional block of JPEG standard?

4. Discuss the Image smoothing filters with its model in the Spatial Domain?

5. What are Image Sharpening filters? Explain the various types of it?

6. Discuss the Diagonalization of circulant and Block circulant matrices.

7. What is the relation between Edge Linking and Boundary Detection? Discuss the three approaches for

Edge Linking?

8. Explain with necessary diagrams how Histogram modeling techniques modify an image?

9. Explain Wiener smoothing filter and its relation with inverse filtering and diffracted limited systems?

10. Explain Huffmann coding Algorithm giving a numerical example?

11. Explain the Constrained Least Square filtering?

12. Describe Image Formation in the eye with Brightness adaption and Discrimination?

13. Explain the two techniques of region representation?

14. Explain the concept of Template Matching and Area Correlation?

15. Define Haar Transform. Derive the same for n=8. What are its properties?

16. For a given orthogonal matrix A and an image U, show that A*TÖ A*=U= original image given A=

1/Ö 2 1 1 U= 1 2

17. Discuss the properties and applications of 1)Hadamard transform

2). Hotelling transform

18. Explain the types of error free compression techniques?

19. Explain the segmentation techniques that are based on finding the regions directly?

20. How is line detected? Explain through the operators?

21. Explain in detail about the color model and color enhancement.

22. Explain how compression is achieved in transform coding and explain the DCT.

23. Discuss about the MPEG standard and compare with JPEG.

24. Explain arithmetic coding and Huffmann coding.

25. Explain various functional block of Digital Image processing?

CS1002- DIP Q.B

ST. ANNE’S COLLEGE OF ENNGINEERING & TECHNOLOGY

ANGUCHETTYPALAYAM, PANRUTI-607 110 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

QUESTION BANK

CS1002- DIGITAL IMAGE PROCESSING

IV YEAR – VIII SEMESTER

UNIT I DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS

PART – A

1. Define Image?

2. What is Dynamic Range?

3. Define Brightness?

4. Define Tapered Quantization?

5. What do you meant by Gray level?

6. Define Resolutions?

7. What is meant by pixel?

8. Define Digital image?

9. What are the steps involved in DIP?

10. What is recognition and Interpretation?

11. Specify the elements of DIP system?

12. Explain the categories of digital storage?

13. What are the types of light receptors?

14. Differentiate photopic and scotopic vision?

15. How cones and rods are distributed in retina?

16. .Define subjective brightness and brightness adaptation?

17. Define weber ratio

18. What is meant by machband effect?

19. What is simultaneous contrast?

20. What is meant by illumination and reflectance?

PART- B 1. Describe the elements of visual perception.

2. Write short notes on sampling and quantization.

3. Describe the functions of elements of digital image processing system with a diagram.

4. Explain the basic relationships between pixels?

5. Explain the properties of 2D Fourier Transform.

6. Explain convolution property in 2D fourier transform.

7. Explain Fast Fourier Transform (FFT) in detail.

8. Explain in detail the different separable transforms

9. Explain Hadamard transformation in detail.

10. Discuss the properties and applications of 1)Hadamard transform II) Discrete Cosine Trnsforms

11. Explain Haar and slash transform in detail.

12. Explain K-L transform in detail.

13. Write a detailed note on walsh transform.

CS1002- DIP Q.B

UNIT II IMAGE ENHANCEMENT TECHNIQUES

PART- A

1. Specify the objective of image enhancement technique.

2. Explain the 2 categories of image enhancement.

3. What is contrast stretching?

4. What is grey level slicing?

5. Define image subtraction.

6. What is the purpose of image averaging?

7. What is meant by masking?

8. Give the formula for negative and log transformation.

9. What is meant by bit plane slicing?

10. What is meant by laplacian filter?

11.What is meant by histogram equalization?

12.Differentiate linear spatial filter and non-linear spatial filter.

13.Give the mask used for high boost filtering?

14 .Define histogram.

15.Write the steps involved in frequency domain filtering.

16.Give the formula for transform function of a Butterworth low pass filter.

17. What do you mean by Point processing?

18. What is Image Negatives?

19. Define Derivative filter?

20. Explain spatial filtering?

21. What is a Median filter?

22. What is maximum filter and minimum filter?

23. Write the application of sharpening filters?

24. Name the different types of derivative filters?

25. What is the advantages of Median filter?

26. Compare spatial and frequency Domain methods.

27.What are the effects of applying Butterworth low pass filter to the noisy image.

PART- B

1. Explain the types of gray level transformation used for image enhancement.

2. What is histogram? Explain histogram equalization.

3. Discuss the image smoothing filter with its model in the spatial domain.

4. What are image sharpening filters? Explain the various types of it.

5. Explain spatial filtering in image enhancement.

6. Explain image enhancement in the frequency domain.

7. Explain Homomorphic filtering in detail.

CS1002- DIP Q.B

UNIT III IMAGE RESTORATION

PART- A

1. What is meant by Image Restoration?

2. What are the two properties in Linear Operator?

3. Explain additivity property in Linear Operator?

4. How a degradation process is modeled?

5. Explain homogenity property in Linear Operator?

6. Give the relation for degradation model for continuous function?

7. What is fredholm integral of first kind? 8. Define circulant matrix?

8. what is concept algebraic approach?

9. What are the two methods of algebraic approach?

10. Define Gray-level interpolation?

11. What is meant by Noise probability density function?

12. Why the restoration is called as unconstrained restoration?

13. Which is the most frequent method to overcome the difficulty to formulate the spatial relocation of

pixels?

14. What are the three methods of estimating the degradation function?

15. What are the types of noise models?

16. Give the relation for rayleigh noise?

17. Give the relation for Gamma noise?

18. Give the relation for Exponential noise?

19. Give the relation for Uniform noise?

20. Write the properties of Singular value Decomposition (SVD)?

21. What is inverse filtering? 24. What is pseudo inverse filter?

22. What is meant by least mean square filter?

23.Draw the model of image degradation process.

PART- B

1.What is the use of wiener filter in image restoration. Explain.

2.What is meant by Inverse filtering? Explain.

3.Explain singular value decomposition and specify its properties.

4.Explain image degradation model /restoration process in detail.

5. What are the two approaches for blind image restoration? Explain in detail.

6. Discuss about Constrained Least square restoration for a digital image in detail.

7. What is image restoration?Explain the degaradation model for continuous function in detail.

CS1002- DIP Q.B

UNIT IV IMAGE COMPRESSION

PART-A

1. What is image compression?

2. What is Data Compression?.

3. What are two main types of Data compression?

5. What are different Compression Methods?

6. Define is coding redundancy?

7. Define interpixel redundancy?

8. What is run length coding?

9. Define compression ratio.

11. Define encoder

12. Define source encoder

13. Define channel encoder

14. What are the types of decoder?

15. What are the operations performed by error free compression?

17. Define Huffman coding

19. Define instantaneous code

20. Define uniquely decodable code

21. Define B2 code

22. Define the procedure for Huffman shift

23. Define arithmetic coding

24. What is bit plane Decomposition?

25. What are three categories of constant area coding?

26.How sub image size selection affect transform coding error.

PART –B

1.What is data redundancy? Explain three basic data redundancy?

2. What is image compression? Explain any four variable length coding compression schemes.

3.Definition of image compression

4. Explain about Image compression model?

5. The source Encoder and Decoder

6.The channel Encoder and Decoder

7. Explain about Error free Compression?

8. Explain about Lossy compression?

9. Explain the schematics of image compression standard JPEG.

10. Diffeentiate between lossless and lossy compression and explain transform coding system with a neat

diagram.

CS1002- DIP Q.B

UNIT V IMAGE SEGMENTATION AND REPRESENTATION

PART-A

1. What is segmentation?

2. Write the applications of segmentation

3. What are the three types of discontinuity in digital image?

4.How the derivatives are obtained in edge detection during formulation?

5 Write about linking edge points.

6.What are the two properties used for establishing similarity of edge p What is edge?

8. Give the properties of the second derivative around an edge?

9. Define Gradient Operator?

10. What is meant by object point and background point?

11. What is global, Local and dynamic or adaptive threshold?

12. Define region growing?

13. Specify the steps involved in splitting and m15. What are the 2 principles steps involved in marker

selection?

14. Define shape numbers

15. Describe Fourier descriptors 16 Define chain codes?

17. What are the demerits of chain code?

18. What is thinning or skeletonizing algorithm?

19. Specify the various image representation approaches

20. What is polygonal approximation method ?

21. Specify the various polygonal approximation methods

22. Name few boundary descriptors

23.Give the Fourier descriptors for the following transformations

24. Define length of a boundary.

25. Define eccentricity and curvature of boundary

PART-B

1. Discuss about region based image segmentation techniques. Compare threshold region based

techniques.

2. Define and explain the various representation approaches?

3.Polygon approximations

4. Explain Boundary descriptors in detail with a neat diagram..

5. Explain regional descriptors

6. Explain the two techniques of region representation

7. Explain the segmentation techniques that are based on finding the regions directly

8. How is line detected? Explain through the operators