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Transcript of Digital Video Processing Matthew Shreve Computer Science and Engineering University of South Florida...
Digital Video Processing
Matthew ShreveComputer Science and Engineering
University of South Florida
Digital Image Processing – Fall 2010 Prof. Dmitry Goldgof
Basics of Video
Static scene capture ImageBring in motion Video
• Image sequence: A 3-D signal– 2 spatial dimensions & 1 time dimension
– Continuous I (x, y, t) discrete I (m, n, tk)
Video Camera
• Frame-by-frame capturing• CCD sensors (Charge-Coupled Devices)
– 2-D array of solid-state sensors– Each sensor corresponds to a pixel– Stored in a buffer and sequentially read out– Widely used
Progressive vs. Interlaced Videos
• Progressive– Every pixel on the screen is refreshed in order (monitors) or
simultaneously (films)
• Interlaced – Refreshed twice every frame; the little gun at the back of your
CRT shoots all the correct phosphors on the even numbered rows of pixels first and then odd numbered rows
– NTSC frame-rate of 29.97 means the screen is redrawn 59.94 times a second
– In other words, 59.94 half-frames per second or 59.94 fields per second
Progressive vs. Interlaced Videos
• How interlaced video could cause problems– Suppose you resize a 720 x 480 interlaced video to 576
x 384 (20% reduction)– How does resizing work?
• takes a sample of the pixels from the original source and blends them together to create the new pixels
– In case of interlaced video, you might end of blending scan lines of two completely different images!
Progressive vs. Interlaced Videos
Image after being resized to 576x384 Some scan lines blended together!
Why Digital?
• “Exactness”– Exact reproduction without degradation– Accurate duplication of processing result
• Convenient & powerful computer-aided processing– Can perform rather sophisticated processing through
hardware or software
• Easy storage and transmission– 1 DVD can store a three-hour movie !!!– Transmission of high quality video through network in
reasonable time
Digital Video Coding
• The basic idea is to remove redundancy in video and encode it
• Perceptual redundancy– The Human Visual System is less sensitive to color and
high frequencies
• Spatial redundancy– Pixels in a neighborhood have close luminance levels
• Low frequency
• How about temporal redundancy?– Differences between subsequent frames can be small.
Shouldn’t we exploit this?
Hybrid Video Coding
• “Hybrid” ~ combination of Spatial, Perceptual, & Temporal redundancy removal
• Issues to be handled– Not all regions are easily inferable from previous frame
• Occlusion ~ solved by backward prediction using future frames as reference
• The decision of whether to use prediction or not is made adaptively
– Drifting and error propagation• Solved by encoding reference regions or frames at constant intervals
of time
– Random access• Solved by encoding frame without prediction at constant intervals of
time
– Bit allocation• according to statistics• constant and variable bit-rate requirement
MPEG combines all of these features !!!
MPEG
• MPEG – Moving Pictures Experts Group– Coding of moving pictures and associated audio
• Picture part– Can achieve compression ratio of about 50:1 through storing only
the difference between successive frames– Even higher compression ratios possible
Bit Rate
• Defined in two ways– bits per second (all inter-frame compression algorithms)– bits per frame (most intra-frame compression algorithms
except DV and MJPEG)
• What does this mean?– If you encode something in MPEG, specify it to be 1.5
Mbps; it doesn’t matter what the frame-rate is, it takes the same amount of space lower frame-rate will look sharper but less smooth
– If you do the same with a codec like Huffyuv or Intel Indeo, you will get the same image quality through all of them, but the smoothness and file sizes will change as frame-rate changes
MPEG-1 Compression Aspects
• Lossless and Lossy compression are both used for a high compression rate
• Down-sampled chrominance – Perceptual redundancy
• Intra-frame compression – Spatial redundancy– Correlation/compression within a frame– Based on “baseline” JPEG compression standard
• Inter-frame compression– Temporal redundancy– Correlation/compression between like frames
• Audio compression– Three different layers (MP3)
• It is clear that we don’t all these bits!– Our previous example illustrated the eye’s sensitivity
to luminance
• We can build a perceptual model– Give more importance to what is perceivable to the
Human Visual System• Usually this is a function of the spatial frequency
Perceptual Redundancy
Fundamentals of JPEG
DCT Quantizer Entropy coder
IDCT Dequantizer Entropy
decoder
Compressed
image data
Encoder
Decoder
Fundamentals of JPEG
• JPEG works on 8×8 blocks• Extract 8×8 block of pixels• Convert to DCT domain• Quantize each coefficient
– Different stepsize for each coefficient• Based on sensitivity of human visual system
• Order coefficients in zig-zag order– Similar frequencies are grouped together
• Run-length encode the quantized values and then use Huffman coding on what is left
Random Access and Inter-frame Compression
Temporal Redundancy – Only perform repeated encoding of the parts of a picture
frame that are rapidly changing– Do not repeatedly encode background elements and still
elements
Random access capability– Prediction that does not depend upon the user accessing the first
frame (skipping through movie scenes, arbitrary point pick-up)
General Considerationsfor Motion Estimation
• Two categories of approaches:– Feature based (more often used in object tracking, 3D
reconstruction from 2D)– Intensity based (based on constant intensity
assumption) (more often used for motion compensated prediction, required in video coding, frame interpolation)
• Three important questions– How to represent the motion field?– What criteria to use to estimate motion parameters?– How to search motion parameters?
Motion Representation
Global:Entire motion field is represented by a few global parameters
Pixel-based:One MV at each pixel, with some smoothness constraint between adjacent MVs.
Region-based:Entire frame is divided into regions, each region corresponding to an object or sub-object with consistent motion, represented by a few parameters.
Block-based:Entire frame is divided into blocks, and motion in each block is characterized by a few parameters.
Also mesh-based (flow of corners, approximated inside)
Pre
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Half-pel Exhaustive Block Matching Algorithm (EBMA)
Examples
Motion Compensated Prediction
• Divide current frame, i, into disjoint 16×16 macroblocks
• Search a window in previous frame, i-1, for closest match
• Calculate the prediction error• For each of the four 8×8 blocks in the
macroblock, perform DCT-based coding• Transmit motion vector + entropy coded
prediction error (lossy coding)
MPEG-1 Video Coding
• Most MPEG1 implementations use a large number of I frames to ensure fast access– Somewhat low compression ratio by itself
• For predictive coding, P frames depend on only a small number of past frames– Using less past frames reduces the propagation error
• To further enhance compression in an MPEG-1 file, introduce a third frame called the “B” frame bi-directional frame– B frames are encoded using predictive coding of only two other
frames: a past frame and a future frame
• By looking at both the past and the future, helps reduce prediction error due to rapid changes from frame to frame (i.e. a fight scene or fast-action scene)
Predictive coding hierarchy:I, P and B frames
• I frames (black) do not depend on any other frame and are encoded separately– Called “Anchor frame”
• P frames (red) depend on the last P frame or I frame (whichever is closer)– Also called “Anchor frame”
• B frames (blue) depend on two frames: the closest past P or I frame, and the closest future P or I frame– B frames are NOT used to predict other B frames, only P frames
and I frames are used for predicting other frames
MPEG-1 Temporal Order of Compression
• I frames are generated and compressed first– Have no frame dependence
• P frames are generated and compressed second– Only depend upon the past I frame values
• B frames are generated and compressed last– Depend on surrounding frames– Forward prediction needed
Adaptive Predictive Coding inMPEG-1
• Coding each block in P-frame– Predictive block using previous I/P frame as reference– Intra-block ~ encode without prediction
• use this if prediction costs more bits than non-prediction• good for occluded area• can also avoid error propagation
• Coding each block in B-frame– Intra-block ~ encode without prediction– Predictive block
• use previous I/P frame as reference (forward prediction)• or use future I/P frame as reference (backward prediction)• or use both for prediction
MPEG Library
• The MPEG Library is a C library for decoding MPEG-1 video streams and dithering them to a variety of color schemes.
• Most of the code in the library comes directly from an old version of the Berkeley MPEG player (mpeg_play)
• The Library can be downloaded fromhttp://starship.python.net/~gward/mpeglib/mpeg_lib-1.3.1.tar.gz
• It works good on all modern Unix and Unix-like platforms with an ANSI C compiler. I have tested it on “grad”.
NOTE - This is not the best library available. But it works good for MPEG-1 and it is fairly easy to use. If you are inquisitive, you should check MPEG Software Simulation Group at http://www.mpeg.org/MPEG/MSSG/ where you can find a free MPEG-2 video coder/decoder.
MPEGe Library
• The MPEGe(ncoding) Library is designed to allow you to create MPEG movies from your application
• The library can be downloaded from the files section ofhttp://groups.yahoo.com/group/mpegelib/
• The encoder library uses the Berkeley MPEG encoder engine, which handles all the complexities of MPEG streams
• As was the case with the decoder, this library can write only one MPEG movie at a time
• The library works good with most of the common image formats– To keep things simple, we will stick to PPM
MPEGe Library Functions
• The library consists of 3 simple functions – MPEGe_open for initializing the encoder. – MPEGe_image called each time you want to add a frame
to the sequence. The format of the image pointed to by image is that used by the SDSC Image library
• SDSC is a powerful library which will allow you to read/write 32 different image types and also contains functions to manipulate them. The source code as well as pre-compiled binaries can be downloaded at ftp://ftp.sdsc.edu/pub/sdsc/graphics/
– MPEGe_close called to end the MPEG sequence. This function will reset the library to a sane state and create the MPEG end sequences and close the output file
Note: All functions return non NULL (i.e. TRUE) on success and Zero (or FALSE) on failure.
Usage Details
• You are not required to write code using the libraries to decode and encode MPEG streams
• Copy the binary executables from– http://www.csee.usf.edu/~mshreve/readframes– http://www.csee.usf.edu/~mshreve/encodeframes
• Usage– To read frames from an MPEG movie (say test.mpg) and store them in a directory
extractframes (relative to your current working directory) with the prefix testframe (to the filename)
• readframes test.mpg extractframes/testframe This will decode all the frames of test.mpg into the directory extractframes with
the filenames testframe0.ppm, testframe1.ppm …– To encode,
• encodeframes 0 60 extractframes/testframe testresult.mpg This will encode images testframe0.ppm to testframe60.ppm from the directory
extractframes into testresult.mpg• In order to convert between PPM and PGM formats, copy the script from
– http://www.csee.usf.edu/~mshreve/batchconvert