The key to high performance in image sequence coding lies in an efficient reduction of the temporal redundancies. For this purpose, motion estimation and compensation techniques have been suc cessfully applied. This paper studies motion estimation algorithms in the context of fi rst generation coding techniques commonly used in digital TV. In this framework, estimating the motion in the scene is not an intrinsic goal. Motion estimation should indeed provide good temporal prediction and simultaneously require low overhead information. More specifically, the aim is to minimize globally the bandwidth corresponding to both the prediction error information and the motion parameters. This paper first clarifies the notion of motion, reviews classical motion estimation tech niques, and outlines new perspectives. Block matching techniques are shown to be the most appropriate in the framework of first generation coding. To overcome the drawbacks characteristic of most block matching techniques, this paper proposes a new locally adaptive multigrid block matching motion estimation technique.This algorithm has been designed taking into account the above aims. It leads to a robust motion field estimation, precise prediction along moving edges and a decreased amount of side information in uniform areas. Furthermore, the algorithm controls the accuracy of the motion estimation procedure in order to optimally balance the amount of information corresponding to the prediction error and to the motion parameters. Experimental results show that the technique results in greatly enhanced visual quality and significant saving in terms of bit rate when compared to classical block matching techniques.

This paper proposes a technique for spatiotemporal segmentation to identify the objects present in the scene represented in a video sequence. This technique processes two consecutive frames at a time. A region-merging approach is used to identify the objects in the scene. Starting from an oversegmentation of the current frame, the objects are formed by iteratively merging regions together. Regions are merged based on their mutual spatiotemporal similarity. The spatiotemporal similarity measure takes both temporal and spatial information into account, the emphasis being on the former. We propose a Modified Kolmogorov-Smirnov test for estimating the temporal similarity. This test efficiently uses temporal information in both the residual distribution and the motion parametric representation. The region-merging process is based on a weighted, directed graph. Two complementary graph-based clustering rules are proposed, namely, the strong rule and the weak rule. These rules take advantage of the natural structures present in the graph. Also, the rules take into account the possible errors and uncertainties reported in the graph. The weak rule is applied after the strong rule. Each rule is applied iteratively, and the graph is updated after each iteration. Experimental results on different types of scenes demonstrate the ability of the proposed technique to automatically partition the scene into its constituent objects.

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This paper proposes a new technique to build a background memory based on mosaicking. More precisely, the technique rst identi es background and foreground regions based on local motion estimates. Camera motion is then estimated on the background by applying a parametric global motion estimation. Finally, after compensating for camera motion, the background content is temporally integrated in longterm memory. The method leads to high coding performances and allows for content-based functionalities.

The problem to segment an image sequence in terms of regions characterized by a coherent motion is among the most challenging in image sequence analysis. This paper proposes a new technique which sequentially renes the segmentation and the motion estimation by combining static segmentation and motion information. Simulation results show the e ciency of the proposed technique.