2014 Summer School on MPEG/VCEG Video. Video Coding Concept

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1 2014 Summer School on MPEG/VCEG Video 1 Video Coding Concept

2 Outline 2 Introduction Capture and representation of digital video Fundamentals of video coding Summary

3 Outline 3 Introduction Capture and representation of digital video Fundamentals of video coding Summary

4 Why do we need video compression? 4 Raw data rate of HDTV video 1920x1080p pels/frame, 3 colors/pel, 8 bits/color, 60 frames/s => GGGGGGGGGG/ss 20 Mbits/s for HDTV channel bandwidth The capabilities of transmission & storage are limited

5 Video/Image Coding Everywhere 5 MPEG-1 Block-based Video Coding (VCD) MPEG-4 AVC/H.264 Scalable Video Coding MPEG-2 Block-based Video Coding With Interlaced tools (DVD) Multi-View Video Coding for 3DTV MPEG-H HEVC/H.265

6 Moore s Law of Video Coding 6 MPEG-4 AVC/H.264 >50%, 5 years MPEG-4 MPEG-2 H.263 From: T. Wiegand, H. Schwarz, A. Joch, F. Kossentini, G. Sullivan, Rate-Constrained coder control and comparison of video coding standards, IEEE Trans. CSVT, 2003

7 Outline 7 Introduction Capture and representation of digital video Fundamentals of video coding Summary

8 Human Vision 8 Any color can be created by combining Red, Green, Blue in varying proportions However, our visual system is less sensitive to color Cone: Perceives color tone Rod: Extracts only luminance

9 Spatial and Temporal Sampling 9 Progressive Sampling CCD/CMOS Mosaic

10 Translation of color space 10 Represent a color image more efficiently by separating the luminance from the color Representing chrominance with a lower resolution R G B Y Cb Cr

11 Chroma Down-sampling 11 4:4:4 4:2:2 4:1:1 4:2:0 4:4:4 4:2:0

12 Video Formats for Different Applications 12

13 Objective Quality Metric 13 Peak Signal-to-Noise Ratio PSNR = 10log For objective video quality measurement, average perframe PSNR is computed as a function of bitrates PSNR = 33.8 PSNR = BitDepth (2 1) MSE 2

14 Subjective Measure 14 Mean Opinion Score Viewer's judgment of quality Methods Double Stimulus Continuous Quality Scale (DSCQS) Double Stimulus Impairment Scale (DSIS) Single Stimulus Continuous Quality Evaluation (SSCQE)

15 Outline 15 Introduction Capture and representation of digital video Fundamentals of video coding Summary

16 How does compression work? 16 Compression is achieved by removing redundancy Temporal and spatial redundancy: temporally adjacent frames are often very similar, so are the neighboring pixels Statistical redundancy: some values occur more frequently than others Temporal Correlation Frame (t-1) Spatial Correlation Frame (t)

17 Encoder and Decoder (CODEC) 17 Video compression (video coding) is the process of compacting or condensing a digital video sequence into a small number of bits Compression involves a complementary pair of systems, a compressor (encoder) and a de-compressor (decoder) Encoder transmit or store Decoder

18 Video Coding System 18 Encoder Analysis Lossy Quantization Lossless Binary Encoding Synthesis De-quantization Binary Decoding Decoder

19 Motion Estimation and Compensation 19 Goal: to remove temporal redundancy Divide a current picture into macroblocks (MB) Find for each MB a best match in the reference frame Transmit the offset (motion vector) and residual block Assumption: 1) translation motion, 2) constant intensity MB Reference Current

20 Motion Compensation Block Size (1/2) 20

21 Motion Compensation Block Size (2/2) 21

22 Remarks 22 The smaller the block size, the less the residual energy Theoretically, it is possible to estimate a motion vector for each pixel However, the extra overhead for vectors may outweigh the benefits of temporal prediction For a compromise between overhead and prediction efficiency, variable block-size motion compensation is often used

23 Variable Block-Size Motion Compensation 23 To adapt block size to picture characteristics Iain Richardson, H.264 and MPEG-4 Video Compression, Wiley, 2003

24 Backward Prediction & Bi-Prediction 24 Some objects in Frame (t) may not appear in Frame (t-1), but can be found in Frame (t+1), due to occlusion Backward prediction from a future reference frame The others may be found in both frames Bi-prediction from two reference frames Frame (t-1) Frame (t) Frame (t+1)

25 Coding Order vs. Display Order 25 Coding order: the order in which pictures are processed Display order: the order in which pictures are displayed 0 (A) 1 (B) 2 (C) 3 (D) 4 (E) 5 (F) Coding order: A, C, B, F, D, E Display order: 0, 1, 2, 3, 4, 5

26 Sub-Pixel Motion Compensation 26 In some cases, a better prediction signal may come from interpolated samples in the reference frame (t) (t-1) Sampling Frame (t) Frame (t-1)

27 Spatial Predictive Coding 27 Goal: to remove spatial redundancy Create prediction signal from previously-coded samples in the same frame, e.g., Intra Coding in H.264/AVC From: Iain Richardson, H.264 and MPEG-4 Video Compression, Wiley, 2003

28 Transform 28 Goal: To remove spatial redundancy further To facilitate scalar quantization scheme Idea: projection onto selected basis functions From: Y. Wang and et al., Video Processing and Communications, Prentice Hall, 2002

29 Choice of Basis Functions 29 Criteria: Minimize inter-dependency between coefficients Compact the energy of input into few coefficients Optimal choice depends on the signal covariance x y x y x y y y x x y = x y 0 1 x x y = 1 x + y 1 1-1

30 2-D Discrete Cosine Transform (DCT) 30 DCT is widely used for video/image coding Zigzag Order

31 Encoding DCT Coefficients 31 Observations: Non-zero coefficients are mostly low-frequency ones High-frequency coefficients tend to be zero up to quantization Example: Run-Length Coding Run: the number of zeros from the last non-zero Length: the non-zero value DCT Coefficients: [5, 0, 0, 2, 3, 0, 0, 4, 0, 0, 0, 1, 0, 0,, 0] Coding Symbols: [5, (2, 2), (0, 3), (2, 4), (3, 1), EOB]

32 Quantization 32 Goal: to reduce the range of a source signal so that it can be represented by fewer bits E.g., rounding a fractional number to the nearest integer Specification Number of reconstructed values Boundary values (b 0, b 1 ) Reconstructed values (g 0, g 1 )

33 Quantizer Design 33 Criterion: to minimize distortion for a fixed number of reconstructed levels Optimal design depends on (1) signal distribution, (2) distortion measure f(x) Q(x) x * * * * * x

34 Scalar vs. Vector Quantization 34 One sample (scalar) or one group of samples (vector)? Example: using 2 bits to represent (x, y) y y * * * * x * * * * x Scalar: 1 bit for x, 1 bit for y Vector: 2 bits for (x, y)

35 Transform + Scalar Quant. vs. Vector Quant. 35 In some cases, Transform + Scalar Quant. can achieve a similar effect to Vector Quant., but with less complexity y * * y * * x x * * y * * x Scalar: 2 bits for x, 0 bit for y Vector: 2 bits for (x, y)

36 Huffman Coding Questions : one player thinks of an object and the other asks questions to determine what that is Goal: to ask as few questions as possible on average Average codeword length: ( )x2= Average codeword length: 0.2x2+0.1x3+0.1x3+0.6x1=1.6

37 Arithmetic Coding 37 Example: encoding ACD Symbol Probability Sub-range A B C D A B C D A B C D A B C D

38 Comparison of Huffman & Arithmetic Coding38 Huffman Coding Arithmetic Coding Optimal entropy Yes No Codeword length Must be integer Not constrained to be an integer Extra storage Yes No Extra transmission time Yes No Encoding delay Yes No

39 Block-based Hybrid Coding Architecture 39 Hybrid: Predictive Coding + Transform Coding Temporal Prediction Spatial Transform Quantization Video - DCT Q Entropy Coder Bit-stream Q -1 {M.V.} M.C. IDCT Entropy Compression M.E. + Frame Buffer

40 Summary 40 Keys to video compression are data redundancy Spatial, temporal and statistics redundancies Block-based hybrid video coding Block-based motion compensated prediction (temporal) Spatial intra prediction (spatial) Residual transform coding (spatial + simple quantization) Quantization (lossy) Huffman coding (statistics)

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