Foundation of Video Coding Part I: Overview and Binary Encoding
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1 Foundation of Video Coding Part I: Overview and Binary Encoding Yao Wang Polytechnic University, Brooklyn, NY Based on: Y. Wang, J. Ostermann, and Y.-Q. Zhang, Video Processing and Communications, Prentice Hall, 2002.
2 Outline Overview of video coding systems Review of probability and information theory concepts Binary encoding Information theory bounds Huffman coding Arithmetic coding Yao Wang, 2006 Coding: Overview and Lossless Coding
3 Components in a Coding System Yao Wang, 2006 Coding: Overview and Lossless Coding 3
4 Video Coding Techniques Based on Different Source Models Waveform-based techniques Content-dependent-techniques Yao Wang, 2006 Coding: Overview and Lossless Coding 4
5 Statistical Characterization of Random Sources Source: a random sequence (discrete time random process), Ex 1: an image that follows a certain statistics F n represents the possible value of the n-th pixel of the image, n=(m,n) f n represents the actual value taken Ex 2: a video that follows a certain statistics F n represents the possible value of the n-th pixel of a video, n=(k,m,n) f n represents the actual value taken Continuous source: F n takes continuous values (analog image) Discrete source: F n takes discrete values (digital image) Stationary source: statistical distribution invariant to time (space) shift Probability distribution probability mass function (pmf) or probability density function (pdf): Joint pmf or pdf: Conditional pmf or pdf: Yao Wang, 2006 Coding: Overview and Lossless Coding 5
6 Entropy of a RV Consider RV F={f 1,f 2,,f K }, with probability p k =Prob.{F= f K } Self-Information of one realization f k : H k = -log(p k ) p k =1: always happen, no information P k ~0: seldom happen, its realization carries a lot of information Entropy = average information: Entropy is a measure of uncertainty or information content, unit=bits Very uncertain -> high information content Yao Wang, 2006 Coding: Overview and Lossless Coding 6
7 Example: Two Possible Symbols Example: Two possible outcomes Flip a coin, F={ head, tail }: p 1 =p 2 =1/2: H=1 (highest uncertainty) If the coin has defect, so that p 1 =1, p 2 =0: H=0 (no uncertainty) More generally: p 1 =p, p 2 =1-p, H=-(p log p+ (1-p) log (1-p)) 0 1/2 1 p Yao Wang, 2006 Coding: Overview and Lossless Coding 7
8 Another Example: English Letters 26 letters, each has a certain probability of occurrence Some letters occurs more often: a, s, t, Some letters occurs less often: q, z, Entropy ~= information you obtained after reading an article. But we actually don t get information at the alphabet level, but at the word level! Some combination of letters occur more often: it, qu, Yao Wang, 2006 Coding: Overview and Lossless Coding 8
9 Joint Entropy Joint entropy of two RVs: Uncertainty of two RVs together N-th order entropy Uncertainty of N RVs together Entropy rate (lossless coding bound) Average uncertain per RV Yao Wang, 2006 Coding: Overview and Lossless Coding 9
10 Conditional Entropy Conditional entropy between two RVs: Uncertainty of one RV given the other RV M-th order conditional entropy Yao Wang, 2006 Coding: Overview and Lossless Coding 10
11 Example: 4-symbol source Four symbols: a, b, c, d pmf: T p = [0.5000,0.2143,0.1703,0.1154] 1 st order conditional pmf: q ij =Prob(f i f j ) 2 nd order pmf: Ex. p(" ab") = p(" a") q(" b"/" a") = 0.5* = Go through how to compute H 1, H 2, H c,1. Yao Wang, 2006 Coding: Overview and Lossless Coding 11
12 Mutual Information Mutual information between two RVs : Information provided by G about F M-th order mutual information (lossy coding bound) Yao Wang, 2006 Coding: Overview and Lossless Coding 12
13 Lossless Coding (Binary Encoding) Binary encoding is a necessary step in any coding system Applied to original symbols (e.g. image pixels) in a discrete source, or converted symbols (e.g. quantized transformed coefficients) from a continuous or discrete source Binary encoding process (scalar coding) Symbol a i Binary Encoding Codeword c i (bit length l i ) Probability table p i Bit rate (bit/symbol): Yao Wang, 2006 Coding: Overview and Lossless Coding 13
14 Bound for Lossless Coding Scalar coding: Assign one codeword to one symbol at a time Problem: could differ from the entropy by up to 1 bit/symbol Vector coding: Assign one codeword for each group of N symbols Larger N -> Lower Rate, but higher complexity Conditional coding (context-based coding) The codeword for the current symbol depends on the pattern (context) formed by the previous M symbols Yao Wang, 2006 Coding: Overview and Lossless Coding 14
15 Binary Encoding: Requirement A good code should be: Uniquely decodable Instantaneously decodable prefix code Yao Wang, 2006 Coding: Overview and Lossless Coding 15
16 Huffman Coding Idea: more frequent symbols -> shorter codewords Algorithm: Huffman coding generate prefix code Can be applied to one symbol at a time (scalar coding), or a group of symbols (vector coding), or one symbol conditioned on previous symbols (conditional coding) Yao Wang, 2006 Coding: Overview and Lossless Coding 16
17 Huffman Coding Example: Scalar Coding Yao Wang, 2006 Coding: Overview and Lossless Coding 17
18 Huffman Coding Example: Vector Coding
19 Huffman Coding Example: Conditional Coding R H C," a" C," a" = , R = , H C," b" = C," b" = R C," c" H = C," c" R = C," d" H C," d" = , R C,1 = , H = C,1 = Yao Wang, 2006 Coding: Overview and Lossless Coding 19
20 Arithmetic Coding Basic idea: Represent a sequence of symbols by an interval with length equal to its probability The interval is specified by its lower boundary (l), upper boundary (u) and length d (=probability) The codeword for the sequence is the common bits in binary representations of l and u A more likely sequence=a longer interval=fewer bits The interval is calculated sequentially starting from the first symbol The initial interval is determined by the first symbol The next interval is a subinterval of the previous one, determined by the next symbol Yao Wang, 2006 Coding: Overview and Lossless Coding 20
21 P(a)=1/2 P(b)=1/4 P(c)=1/4 Encoding: 1/2 Decoding:
22 Implementation of Arithmetic Coding Previous example is meant to illustrate the algorithm in a conceptual level Require infinite precision arithmetic Can be implemented with finite precision or integer precision only For more details on implementation, see Witten, Neal and Cleary, Arithmetic coding for data compression, J. ACM (1987), 30: Sayood, Introduction to Data Compression, Morgan Kaufmann, 1996 Yao Wang, 2006 Coding: Overview and Lossless Coding 22
23 Huffman vs. Arithmetic Coding Huffman coding Convert a fixed number of symbols into a variable length codeword Efficiency: To approach entropy rate, must code a large number of symbols together Used in all image and video coding standards Arithmetic coding Convert a variable number of symbols into a variable length codeword Efficiency: N is sequence length Can approach the entropy rate by processing one symbol at a time Easy to adapt to changes in source statistics Integer implementation is available, but still more complex than Huffman coding with a small N Not widely adopted in the past (before year 2000) Used as advanced options in image and video coding standards Becoming standard options in newer standards (JPEG2000,H.264) Yao Wang, 2006 Coding: Overview and Lossless Coding 23
24 Summary Coding system: original data -> model parameters -> quantization-> binary encoding Waveform-based vs. content-dependent coding Characterization of information content by entropy Entropy, Joint entropy, conditional entropy Mutual information Lossless coding Bit rate bounded by entropy rate of the source Huffman coding: Scalar, vector, conditional coding can achieve the bound only if a large number of symbols are coded together Huffman coding generates prefix code (instantaneously decodable) Arithmetic coding Can achieve the bound by processing one symbol at a time More complicated than scalar or short vector Huffman coding Yao Wang, 2006 Coding: Overview and Lossless Coding 24
25 Homework Reading assignment: Sec (excluding Sec ,8.8.8) Written assignment Prob. 8.1,8.3,8.5,8.6,8.7 (for Prob. 8.7, you don t have to complete the encoding for the entire sequence. Do the first few letters.) Yao Wang, 2006 Coding: Overview and Lossless Coding 25
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