Spatial and Temporal Data Mining V. Megalooikonomou Data Compression.
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Transcript of Spatial and Temporal Data Mining V. Megalooikonomou Data Compression.
Spatial and Temporal Data Mining
V. Megalooikonomou
Data Compression
General Overview
Data Compression Run Length Coding Huffman Coding PCM, DPCM, ADPCM Quantization
Scalar quantization Vector quantization
Image Compression Video Compression
Data Compression
Why data compression? Storing or transmitting multimedia data requires large space or
bandwidth The size of one hour 44K sample/sec 16-bit stereo (two
channels) audio is 3600x44000x2x2= 633.6MB, which can be recorded on one CD (650 MB). MP3 compression can reduce this number by factor of 10
The size of a 500x500 color image is 750KB without compression (JPEG can reduced this by a factor of 10 to 20)
The size of one minute real-time, full size, color video clip is 60x30x640x480x3= 1.659GB. A two-hour movie requires 200GB. MPEG2 compression can bring this number down to 4.7 GB (DVD)
Data Compression
Run length coding
Example: A scanline of a binary image is 00000 00000 00000
00000 00010 00000 00000 01000 00000 00000 Total of 50 bits However, strings of consecutive 0’s or 1’s can be
represented more efficiently 0(23) 1(1) 0(12) 1(1) 0(13) If the counts can be represented using 5 bits, then we
can reduce the amount of data to 5+5*5=30 bits. A compression ratio of 40%
Huffman coding
The basic idea behind Huffman coding algorithm is to assign shorter codewords to more frequently used symbols
Example: let there be 4 letters in language “A”, “B”, “S”, “Z”
To uniquely encode each letter, we need two bits: A- 00 B-01 S-10 Z-11 A message “AAABSAAAAZ” is encoded with 20
bits Now how about assign A- 0, B-100, S-101, Z-11 The same message can be encoded using 15 bits
Huffman coding
Given a set of N symbols S={si, i=1,…N} with probabilities of occurrence Pi, i=1,…N, find the optimal encoding of the symbol to achieve the minimum transmission rate (bits/symbol)
Algorithm: Each symbol is a leaf node in a tree Combining the two symbols or composite symbols with the
least probabilities to form a new parent composite symbol, which has the combined probabilities. Assign a bit 0 and 1 to the two links
Continue this process until all symbols are merged into one root node (bottom up)
For each symbol, the sequence of the 0s and 1s from the root node to the symbol is the codeword
Pulse code modulation
The process of digitizing audio signal is called pulse code modulation Sampling the analog waveform at a minimum rate Each sample is quantized using a fixed number of
bits To reduce the amount of data, we can
Reduce the sampling rate Reduce the number of bits per sample
Differential Pulse Code Modulation (DPCM)
Encode the changes between consecutive samples Example
The value of the differences between samples are much smaller than those of the original samples. Less bits are used to encode the signal (e.g., 7 bits instead of 8 bits)
For decoding the difference is added to the previous sample to obtain the value of the current sample. Lossless coding is achieved
Adaptive Differential Pulse Code Modulation (ADPCM)
One observation is that a small difference between samples happens more often than large changes
An Entropy coding method such the Huffman coding scheme can be used to encode the difference for additional efficiency The probabilities of occurrence of different differences are
first obtained using a large data base Huffman coding is used to determine the codeword for each
difference The codeword is fixed and made available to decoders
Linear Predictive Coding (LPC)
In DPCM, the value of the current sample is guessed based on the previous sample. Can a better prediction be made ?
Yes! For example, we can use the previous two samples to predict the current one
LPC is more general than DPCM. It exploits the correlation between multiple consecutive samples
General Overview
Data Compression Run Length Coding Huffman Coding PCM, DPCM, ADPCM Quantization
Scalar quantization Vector quantization
Image Compression Video Compression
Quantization
One-dimensional quantizer interval endpoints and levels indicated on a horizontal line
Quantization is the discretization of a continuous-alphabet source (signal)
X: original value, X’: codeword, Q: quantizer Distortion:
d(X,X’): a measure of overall quality degradation due to Q Mean Squared Error (MSE): E[(X-X’)2]
Scalar Quantization
One-dimensional quantizer interval endpoints and levels indicated on a horizontal line
Approximates a source symbol by its closest representative from a codebook
An N-point scalar quantizer Q is a mapping: Q: R C where R is the real line and C={y1, y2,…, yN} R is the
codebook of size N Q(x)=D(E(x)) where E: R I is the encoder and D: IC is the decoder I={1,2,…,N}
Vector Quantization
VQ: a generalization of scalar quantization to quantization of a vector
VQ is superior to scalar quantization. Why?
Vector Quantization
VQ: a generalization of scalar quantization to quantization of a vector
VQ is superior to scalar quantization. Why? Exploits linear and non-linear dependence that exists among the
components of a vector VQ is superior even when the components of the random vector
are statistically independent of each other. How? A vector quantizer Q of dimension k and size N is a
mapping from a vector (or a “point” in Rk), into a finite set C={y1, y2,…, yN}, yiRk, the codebook of size N
Q: RkC It partitions Rk into N regions or cells, Ri for iJ{1,2,
…,N} Ri={x Rk: Q(x)=yi}
Motivation for Vector Quantization
Scalar quantization
Plot two successive values as a single vector (x(1), x(2))
Scalar quantization
Vector quantization
Scalar vs Vector Quantization
Vector Quantization - Design
The goal is to find: A codebook (decoder) – representation
levels A partition rule (encoder) – decision levels
To maximize an overall measure of performance
VQ Design – Optimality Conditions
Nearest Neighbor Condition
For a given codebook, C, the optimal regions {Ri: i=1,…,N} satisfy the condition:
Ri {x: d(x,yi) d(x,yj); j}
That is Q(x)=yi only if d(x,yi) d(x,yj) j
VQ Design – Optimality Conditions
Centroid Condition
For given partition regions {Ri:i=1,…,N} the optimal codewords satisfy the condition:
yi = cent(Ri) For the SE measure, the centroid of a set R is the
arithmetic average:
for R={xi : i = 1,…,|R|}
R
iix
RRcent
1
1)(
VQ Design – The Generalized Lloyd Algorithm (GLA)
It produces a locally optimal codebook from a training sequence, T. It starts with an initial codebook and iteratively improves it.
Lloyd Iteration Given a codebook Cm={yi} generate an improved
codebook Cm+1 as follows: Partition T into cells Rj using the Nearest Neighbor Condition:
Ri={x : d(x,yi) d(x,yi); ji} Using the Centroid Condition compute the centroids of the cells
just found to obtain the new codebook, Cm+1={cent(Ri)}
Compute the average distortion for Cm+1 , Dm+1. If the fractional drop (Dm- Dm+1) / Dm is below a certain threshold stop, else continue with mm+1
VQ Design – The Generalized Lloyd Algorithm (GLA)
To solve the initial codebook generation problem a partition split mechanism is used. How?
VQ Design – The Generalized Lloyd Algorithm (GLA)
To solve the initial codebook generation problem a partition split mechanism is used. How?
Starts with a codebook containing only one codeword (which one?) In each repetition and before the application of the Lloyd iteration, it doubles the number of codewords from the previous iteration
See: http://www.data-compression.com/vq.html#animation
General Overview
Data Compression Run Length Coding Huffman Coding PCM, DPCM, ADPCM Quantization
Scalar quantization Vector quantization
Image Compression Video Compression
Image Compression
From the 1D case, we observe that data compression can be achieved by exploiting the correlation between samples
This idea is applicable to 2D signals as well. Instead of predicting sample values, we can use the so
called transformation method to obtain a more compact representation
Discrete Cosine Transform (DCT) DCT is the real part of the 2D Fourier Transform
Discrete Cosine Transform (DCT)
DCT
Inverse DCT
DCT transform of 2D Images
DCT Example
DCT of images can also be considered as the projection
of the original image into the DCT basis functions. Each basis function is in the form of
DCT transform of 2D Images
The basis functions for an 8x8 DCT
DCT compression of 2D Images
After DCT compression, only a few DCT coefficients have large values
We need to: Quantize the DCT coefficients Encode the position of the large coefficients Compress the value of the coefficients