motion estimation and compensation for very low bitrate video coding

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64 Chapter 2. Motion in the Framework of Video Coding x 0 y 0 ! = = Cx cos x Cx sin x a1 a 2 a 3 a 4 ! x y ,Cy sin y ! Cy cos y + ! a5 a 6 : ! : x y ! + tx ty ! (2.30) The a ne transform results from the orthographic projection of the motion of a planar surface. Under perspective projection, an eightparameter perspective transform is built: x 0 = a 1 + a 2x + a 3y 1+a 7x + a 8y ; y 0 = a 4 + a 5x + a 6y 1+a 7x + a 8y : Another commonly used transform is the bilinear transform: x 0 = a 1x + a 2y + a 3xy + a 4 y 0 = a 5x + a 6y + a 7xy + a 8 (2.31) : (2.32) Higher level models also take into account acceleration e ects. Sanson [117] for instance proposes a twelve-parameter model: x 0 y 0 ! = x ax ax y ay x ay y ! x y ! + x b 2 x bx2 y bxy x bxy y by2 x by2 y ! 0 B @ x2 xy y2 1 C A + (2.33) Because of the presence of numerous moving and possibly overlapping objects in the scene, the above parametric models do not hold, in general, throughout the whole image plane. A solution to this problem is provided assuming that every object is characterized by its motion parameters. It leads to the \chicken-and-egg" combined segmentation & estimation problem. Away toovercome it is to use warping techniques (cf. Section 2.6) that have successfully implemented a matching methodology. 2.4.6 Within a Transform Domain Estimation methods based on spatio-temporal lters over several pictures have recently been implemented. They are based on the property tx ty ! :
2.5 The Block-Matching Algorithm (BMA) 65 of the Fourier transform to concentrate the energy within a few coefcients of the transformed frequential domain. The motion estimation can then be achieved by analyzing the importance and the variation of the temporal frequency !t. However, the main limitation of the use of such techniques for coding purposes is that they require more than two successive pictures so to provide a reliable analysis. 2.5 The Block-Matching Algorithm (BMA) Since its introduction by Jain and Jain in 1981, the Block Matching Algorithm (BMA, [50]) has emerged as the ME technique achieving the best compromise between complexity and quality: a fast estimation procedure allows obtaining a block-based motion eld that is transmitted at low-cost. An appropriate choice of the block size o ers one a compromise between adaptation to small moving objects (performed by small blocks) and robustness against noise (performed by large blocks). These properties have granted the BMA to be included in most video standards like H.263 [96], MPEG-1,2 [62] and MPEG-4 [104]. 2.5.1 BMA Principle The principle of the BMA is to apply a translational motion model to subblocks of the image. For every block, the matching measure is based on the Displaced Frame Di erence (DFD). Fuh and Maragos [36] thereafter consider the BMA and its two sole free parameters (the translation vector) as a particular case of more elaborate models. Its implementation in most standards follows the backward procedure (cf. Section 2.3.2.2): 1. the image I(t) is divided into a set of subblocks of size (K; L); 2. for every subblock, the origin of the block in the reference image I(t , 1) is searched for within a search area kuk u, kvk v (cf. gure 2.15) according to one of the criteria presented in Section 2.3.1.1. 3. compensation is achieved by reconstructing ^I(t) with the blocks of I(t , 1) designed by the motion vectors.
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LABORATOIRE DE TELECOMMUNICATIONS E
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Avant-propos Mener a bien une these
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Resume L'estimation du mouvement re
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x 2.1.1 Apparent versus Real Motion
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xii A VLBR-like video sequences 167
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2 Introduction Ancient Greece initi
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4 Introduction Meanwhile, the desir
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6 Introduction However, one can que
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8 Introduction compensation in a vi
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10 Introduction Once the proposed s
Page 25 and 26: Chapter 1 Digital Video Coding at V
Page 27 and 28: 1.2 Video Coding 15 These di erence
Page 29 and 30: 1.2 Video Coding 17 { The redundanc
Page 31 and 32: 1.2 Video Coding 19 exhaustive use
Page 33 and 34: 1.4 Some (Very-) Low BitRate Codecs
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Page 52 and 53: 40 Chapter 2. Motion in the Framewo
Page 90 and 91: 78 Chapter 3. Multiscale Block Matc
Page 107 and 108: Chapter 4 Image Pre-Processing for
Page 109 and 110: 4.1 Intuitive Rationale 97 (a) (b)
Page 111 and 112: 4.1 Intuitive Rationale 99 (a) (b)
Page 113 and 114: 4.2 Rate Distortion Conditions 101
Page 119 and 120: 4.3 Experimental Results 107 Pre-pr
Page 121 and 122: 4.3 Experimental Results 109 Mean P
Page 123 and 124: 4.3 Experimental Results 111 From t
Page 125 and 126: 4.3 Experimental Results 113 Origin
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4.3 Experimental Results 115 Origin
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4.3 Experimental Results 117 Origin
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4.4 Conclusion 119 4.4 Conclusion T
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122 Chapter 5. Mesh-Based Motion Co
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162 Conclusion so as to adapt the s
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164 Conclusion gain is not as obvio
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Appendix A VLBR-like video sequence
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VLBR-like video sequences 169 Coast
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Appendix B Rate Distortion theory A
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B.1 Information Theory 173 B.1 Info
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B.2 Discrete Memoryless Sources and
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B.3 Rate Distortion Function 177 B.
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B.4 Extension to Moving Pictures 17
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B.4 Extension to Moving Pictures 18
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184 Chapter C. Markov Random Fields
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186 Chapter C. Markov Random Fields
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Appendix D Complements to Chapter 5
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D.2 Pseudo-code of the implementati
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Bibliography [1] COST 211ter Simula
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Bibliography 205 [21] E. Decenciere
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Bibliography 207 [41] R.M. Gray. Ve
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Bibliography 209 [65] B. Macq, M.P.
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Bibliography 211 [86] H.G. Musmann,
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Bibliography 213 [110] M.P. Queluz
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Bibliography 215 [132] F. Vermaut,
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motion estimation and compensation for very low bitrate video coding

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