This book discusses in detail the basic algorithms of video compression that are widely used in modern video codec. The authors dissect complicated specifications and present material in a way that gets readers quickly up to speed by describing video compression algorithms succinctly, without going to the mathematical details and technical specifications. For accelerated learning, hybrid codec structure, inter- and intra- prediction techniques in MPEG-4, H.264/AVC, and HEVC are discussed together. In addition, the latest research in the fast encoder design for the HEVC and H.264/AVC is also included.
Introduction.- Hybrid video codec structure.- Intra Prediction.- Inter Prediction.- Rate distortion cost optimization.- Fast Prediction Techniques.- Conclusion.
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1 | (12) |
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1.1 Background and Need for Video Compression |
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1 | (1) |
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1.2 Classifications of the Redundancies |
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2 | (3) |
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1.2.1 Statistical Redundancy |
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2 | (3) |
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1.2.2 Psycho-Visual Redundancy |
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5 | (1) |
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5 | (3) |
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1.4 Brief History About Compression Standards |
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8 | (2) |
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10 | (3) |
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11 | (2) |
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2 Hybrid Video Codec Structure |
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13 | (18) |
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13 | (3) |
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2.1.1 High-Level Picture Partitioning |
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13 | (3) |
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16 | (7) |
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2.2.1 H.264/AVC Block Partitioning |
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17 | (1) |
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2.2.2 HEVC Block Partitioning |
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18 | (5) |
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23 | (1) |
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23 | (5) |
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23 | (2) |
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2.4.2 Sample Adaptive Offset |
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25 | (3) |
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28 | (3) |
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28 | (1) |
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29 | (1) |
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30 | (1) |
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3 Intra-prediction Techniques |
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31 | (8) |
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31 | (1) |
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3.2 Intra-prediction Modes in H.264/AVC |
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32 | (2) |
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3.3 Intra-prediction Modes in HEVC |
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34 | (3) |
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34 | (2) |
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3.3.2 DC and Planer Prediction |
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36 | (1) |
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3.3.3 Reference Sample Smoothing and Boundary Value Smoothing |
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37 | (1) |
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3.4 Lossless Intra-prediction Using DPCM |
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37 | (2) |
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38 | (1) |
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4 Inter-prediction Techniques |
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39 | (14) |
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39 | (3) |
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4.2 Uni- and Bidirectional Predictions |
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42 | (2) |
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4.3 Complexity in the Inter-prediction |
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44 | (2) |
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4.4 Different Inter-prediction Modes |
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46 | (2) |
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48 | (2) |
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4.6 Motion Vector Prediction |
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50 | (3) |
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53 | (10) |
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53 | (1) |
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5.2 Classical Theory of RD Cost |
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54 | (1) |
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5.3 Distortion Measurement Technique |
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55 | (2) |
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5.3.1 Mean of Squared Error |
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55 | (1) |
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5.3.2 Mean of Absolute Difference |
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56 | (1) |
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5.3.3 Sum of Absolute Difference |
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57 | (1) |
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5.4 Calculating A for the RD Cost Function |
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57 | (6) |
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61 | (2) |
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6 Fast Prediction Techniques |
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63 | |
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6.1 Need for the Fast Prediction Algorithms |
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63 | (1) |
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6.2 Fast Options in HEVC Encoder |
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64 | (3) |
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6.2.1 Early CU Termination |
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64 | (1) |
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6.2.2 Early Skip Detection |
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65 | (1) |
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6.2.3 CBF Fast Mode Setting |
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66 | (1) |
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6.2.4 Fast Decision for Merge RD Cost |
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66 | (1) |
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6.3 Block Matching Algorithm |
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67 | (3) |
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70 | (1) |
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6.5 Unsymmetrical-Cross Multihexagon-Grid Search |
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70 | (1) |
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70 | (2) |
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6.7 Enhanced Predictive Zonal Search |
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72 | (2) |
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74 | (3) |
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6.9 Fixed Search Patterns |
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77 | (1) |
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6.10 Search Patterns Based on Block Correlation |
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78 | (1) |
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6.11 Search Patterns Based on Motion Classification |
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79 | (1) |
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6.12 Prediction-Based Fast Algorithms |
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79 | (2) |
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6.13 Improved RD Cost-Based Algorithms |
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81 | (1) |
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6.14 Efficient Filter-Based Algorithms |
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82 | (1) |
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6.15 Improved Transform-Based Algorithms |
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82 | |
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83 | |
Professor Byung-Gyu Kim received his BS degree from Pusan National University, Korea, in 1996 and an MS degree from Korea Advanced Institute of Science and Technology(KAIST) in 1998. In 2004, he received a Ph.D. degree in the Department of Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology (KAIST). In March 2004, he joined in the real-time multimedia research team at the Electronics and Telecommunications Research Institute (ETRI), Korea where he was a senior researcher. In February 2009, he joined the Department of Computer Engineering at SunMoon University, Korea where he is currently a professor. He is also serving as Associate Editor of Circuits, Systems and Signal Processing Journal, Journal of Real-Time Image Processing, Journal of Supercomputing, and other international journals. He has published over 115 international journal and conference papers, patents in his field. His research interests include image and video object segmentation for the content-based image coding, video coding techniques, wireless multimedia sensor network, embedded multimedia communication, and intelligent information system for image signal processing.
Dr. Kalyan Goswami received his B.Tech degree from Kalyani University, India in 2004. From 2004 to 2007 he worked at Cognizant Technology Solutions India Pvt. Ltd., as a programmer analyst. He received a MS degree from Indian Institute of Technology (IIT), Kharagpur, India in 2011 and a Ph.D degree from the Department of Computer Engineering from Sunmoon University, South Korea in 2015. He has published 19 international journal and conference papers in his field. His research interests include video processing, video coding techniques, High Efficiency Video Coding (HEVC), object detection and tracking for video sequences.
Lisainfo e-raamatute kohta