motion estimation and compensation for very low bitrate video coding

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144 Chapter 5. Mesh-Based Motion Compensation vertices (see the right side of the square on gure 5.14(c) and (e)). Moreover, if one of the used block has been badly estimated the compensated image is drastically debased. It is for instance the case for the top left corner of the square on gure 5.13(e) because the displacement of the corresponding 16 16 block has been wrongly estimated by the BMA. A more global interpolation technique which would take the whole available information into account and not only a few arbitrary vectors should be considered. It would also have toachieve some smoothing so as to avoid local traps. Figure 5.15 4 presents some very well-known interpolation techniques. The original data set consists in assigning each pixel the sum of the squares of its x and y coordinates (represented in the color spectrum - low value is blue, high is red). Nearest neighbor interpolation has already been commented on. Linear interpolation is a technique that works best for datasets with a small percentage of unknown values and is well-suited for separable problems. Interpolation of motion vector elds is not separable. Kernel smoothing is a technique which, as its name suggests, smoothes the data so as to provide a data set free of irregularities. Although one here wants to lower the impact of wrong vectors, it is also important tokeep changes of motion along object borders. Moreover, kernel smoothing does not preserve original data values. Both these drawbacks are solved by weighted interpolation which takes the various known data points into account according to a distribution function which relies on a physical distance between these points and the point to be estimated. Another theory of 2-D interpolation has been established by the South African mining engineer Krige [58]. In the eld of geostatistics and hydrosciences, this technique is referred to as kriging. The theory has been further developed by Matheron [78]. Basically, kriging is a weighted interpolation technique which looks at statistical distances rather than physical ones. Kriging is a very powerful method for taking into account individual elements, like the \clustering e ect" of the red point of gure 5.16. However, kriging requires a model of statistical properties (a variogram in the kriging terminology) so as to de ne the a priori \shape" of the data covariance. Such avariogram would be very di cult to establish for motion vectors since one simultaneously wishes to obtain a smooth 4 Images come from the following Web site: http://www.fortner.com.
5.3 Motion Transcription 145 Sample data from original Nearest neighbor Linear interpolation Kernel smoothing Weighted interpolation Figure 5.15: Various interpolation techniques Sample data Kriging Weighted interpolation Figure 5.16: Comparison between kriging and weighted interpolation vector eld within objects while allowing motion changes from one object to another. Inspired by kriging, Decenciere, de Fouquet and Meyer have de ned a technique of inverse kriging [22] that has already been successfully
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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
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Chapter 1 Digital Video Coding at V
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1.2 Video Coding 15 These di erence
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1.2 Video Coding 17 { The redundanc
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1.2 Video Coding 19 exhaustive use
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1.4 Some (Very-) Low BitRate Codecs
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1.5 Discussion 37 1.5 Discussion Th
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40 Chapter 2. Motion in the Framewo
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Page 179 and 180: Appendix A VLBR-like video sequence
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Page 183 and 184: Appendix B Rate Distortion theory A
Page 185 and 186: B.1 Information Theory 173 B.1 Info
Page 187 and 188: B.2 Discrete Memoryless Sources and
Page 189 and 190: B.3 Rate Distortion Function 177 B.
Page 191 and 192: B.4 Extension to Moving Pictures 17
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Page 201 and 202: 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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