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Li, Man Ho. / Thesis submitted in: November 2006. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 137-143). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.3 / Chapter 1.2 --- The objectives of this thesis --- p.4 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis structure --- p.6 / Chapter 2 --- Digital video compression --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- Fundamentals of lossy video compression --- p.9 / Chapter 2.2.1 --- Video compression and human visual systems --- p.10 / Chapter 2.2.2 --- Representation of color --- p.10 / Chapter 2.2.3 --- Sampling methods - frames and fields --- p.11 / Chapter 2.2.4 --- Compression methods --- p.11 / Chapter 2.2.5 --- Motion estimation --- p.12 / Chapter 2.2.6 --- Motion compensation --- p.13 / Chapter 2.2.7 --- Transform --- p.13 / Chapter 2.2.8 --- Quantization --- p.14 / Chapter 2.2.9 --- Entropy Encoding --- p.14 / Chapter 2.2.10 --- Intra-prediction unit --- p.14 / Chapter 2.2.11 --- Deblocking filter --- p.15 / Chapter 2.2.12 --- Complexity analysis of on different com- pression stages --- p.16 / Chapter 2.3 --- Motion estimation process --- p.16 / Chapter 2.3.1 --- Block-based matching method --- p.16 / Chapter 2.3.2 --- Motion estimation procedure --- p.18 / Chapter 2.3.3 --- Matching Criteria --- p.19 / Chapter 2.3.4 --- Motion vectors --- p.21 / Chapter 2.3.5 --- Quality judgment --- p.22 / Chapter 2.4 --- Block-based matching algorithms for motion estimation --- p.23 / Chapter 2.4.1 --- Full search (FS) --- p.23 / Chapter 2.4.2 --- Three-step search (TSS) --- p.24 / Chapter 2.4.3 --- Two-dimensional Logarithmic Search Algorithm (2D-log search) --- p.25 / Chapter 2.4.4 --- Diamond Search (DS) --- p.25 / Chapter 2.4.5 --- Fast full search (FFS) --- p.26 / Chapter 2.5 --- Complexity analysis of motion estimation --- p.27 / Chapter 2.5.1 --- Different searching algorithms --- p.28 / Chapter 2.5.2 --- Fixed-block size motion estimation --- p.28 / Chapter 2.5.3 --- Variable block size motion estimation --- p.29 / Chapter 2.5.4 --- Sub-pixel motion estimation --- p.30 / Chapter 2.5.5 --- Multi-reference frame motion estimation . --- p.30 / Chapter 2.6 --- Picture quality analysis --- p.31 / Chapter 2.7 --- Summary --- p.32 / Chapter 3 --- Arithmetic for video encoding --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Number systems --- p.34 / Chapter 3.2.1 --- Non-redundant Number System --- p.34 / Chapter 3.2.2 --- Redundant number system --- p.36 / Chapter 3.3 --- Addition/subtraction algorithm --- p.38 / Chapter 3.3.1 --- Non-redundant number addition --- p.39 / Chapter 3.3.2 --- Carry-save number addition --- p.39 / Chapter 3.3.3 --- Signed-digit number addition --- p.40 / Chapter 3.4 --- Bit-serial algorithms --- p.42 / Chapter 3.4.1 --- Least-significant-bit (LSB) first mode --- p.42 / Chapter 3.4.2 --- Most-significant-bit (MSB) first mode --- p.43 / Chapter 3.5 --- Absolute difference algorithm --- p.44 / Chapter 3.5.1 --- Non-redundant algorithm for absolute difference --- p.44 / Chapter 3.5.2 --- Redundant algorithm for absolute difference --- p.45 / Chapter 3.6 --- Multi-operand addition algorithm --- p.47 / Chapter 3.6.1 --- Bit-parallel non-redundant adder tree implementation --- p.47 / Chapter 3.6.2 --- Bit-parallel carry-save adder tree implementation --- p.49 / Chapter 3.6.3 --- Bit serial signed digit adder tree implementation --- p.49 / Chapter 3.7 --- Comparison algorithms --- p.50 / Chapter 3.7.1 --- Non-redundant comparison algorithm --- p.51 / Chapter 3.7.2 --- Signed-digit comparison algorithm --- p.52 / Chapter 3.8 --- Summary --- p.53 / Chapter 4 --- VLSI architectures for video encoding --- p.54 / Chapter 4.1 --- Introduction --- p.54 / Chapter 4.2 --- Implementation platform - (FPGA) --- p.55 / Chapter 4.2.1 --- Basic FPGA architecture --- p.55 / Chapter 4.2.2 --- DSP blocks in FPGA device --- p.56 / Chapter 4.2.3 --- Advantages employing FPGA --- p.57 / Chapter 4.2.4 --- Commercial FPGA Device --- p.58 / Chapter 4.3 --- Top level architecture of motion estimation processor --- p.59 / Chapter 4.4 --- Bit-parallel architectures for motion estimation --- p.60 / Chapter 4.4.1 --- Systolic arrays --- p.60 / Chapter 4.4.2 --- Mapping of a motion estimation algorithm onto systolic array --- p.61 / Chapter 4.4.3 --- 1-D systolic array architecture (LA-ID) --- p.63 / Chapter 4.4.4 --- 2-D systolic array architecture (LA-2D) --- p.64 / Chapter 4.4.5 --- 1-D Tree architecture (GA-1D) --- p.64 / Chapter 4.4.6 --- 2-D Tree architecture (GA-2D) --- p.65 / Chapter 4.4.7 --- Variable block size support in bit-parallel architectures --- p.66 / Chapter 4.5 --- Bit-serial motion estimation architecture --- p.68 / Chapter 4.5.1 --- Data Processing Direction --- p.68 / Chapter 4.5.2 --- Algorithm mapping and dataflow design . --- p.68 / Chapter 4.5.3 --- Early termination scheme --- p.69 / Chapter 4.5.4 --- Top-level architecture --- p.70 / Chapter 4.5.5 --- Non redundant positive number to signed digit conversion --- p.71 / Chapter 4.5.6 --- Signed-digit adder tree --- p.73 / Chapter 4.5.7 --- SAD merger --- p.74 / Chapter 4.5.8 --- Signed-digit comparator --- p.75 / Chapter 4.5.9 --- Early termination controller --- p.76 / Chapter 4.5.10 --- Data scheduling and timeline --- p.80 / Chapter 4.6 --- Decision metric in different architectural types . . --- p.80 / Chapter 4.6.1 --- Throughput --- p.81 / Chapter 4.6.2 --- Memory bandwidth --- p.83 / Chapter 4.6.3 --- Silicon area occupied and power consump- tion --- p.83 / Chapter 4.7 --- Architecture selection for different applications . . --- p.84 / Chapter 4.7.1 --- CIF and QCIF resolution --- p.84 / Chapter 4.7.2 --- SDTV resolution --- p.85 / Chapter 4.7.3 --- HDTV resolution --- p.85 / Chapter 4.8 --- Summary --- p.86 / Chapter 5 --- Results and comparison --- p.87 / Chapter 5.1 --- Introduction --- p.87 / Chapter 5.2 --- Implementation details --- p.87 / Chapter 5.2.1 --- Bit-parallel 1-D systolic array --- p.88 / Chapter 5.2.2 --- Bit-parallel 2-D systolic array --- p.89 / Chapter 5.2.3 --- Bit-parallel Tree architecture --- p.90 / Chapter 5.2.4 --- MSB-first bit-serial design --- p.91 / Chapter 5.3 --- Comparison between motion estimation architectures --- p.93 / Chapter 5.3.1 --- Throughput and latency --- p.93 / Chapter 5.3.2 --- Occupied resources --- p.94 / Chapter 5.3.3 --- Memory bandwidth --- p.95 / Chapter 5.3.4 --- Motion estimation algorithm --- p.95 / Chapter 5.3.5 --- Power consumption --- p.97 / Chapter 5.4 --- Comparison to ASIC and FPGA architectures in past literature --- p.99 / Chapter 5.5 --- Summary --- p.101 / Chapter 6 --- Conclusion --- p.102 / Chapter 6.1 --- Summary --- p.102 / Chapter 6.1.1 --- Algorithmic optimizations --- p.102 / Chapter 6.1.2 --- Architecture and arithmetic optimizations --- p.103 / Chapter 6.1.3 --- Implementation on a FPGA platform . . . --- p.104 / Chapter 6.2 --- Future work --- p.106 / Chapter A --- VHDL Sources --- p.108 / Chapter A.1 --- Online Full Adder --- p.108 / Chapter A.2 --- Online Signed Digit Full Adder --- p.109 / Chapter A.3 --- Online Pull Adder Tree --- p.110 / Chapter A.4 --- SAD merger --- p.112 / Chapter A.5 --- Signed digit adder tree stage (top) --- p.116 / Chapter A.6 --- Absolute element --- p.118 / Chapter A.7 --- Absolute stage (top) --- p.119 / Chapter A.8 --- Online comparator element --- p.120 / Chapter A.9 --- Comparator stage (top) --- p.122 / Chapter A.10 --- MSB-first motion estimation processor --- p.134 / Bibliography --- p.137

Image/video segmentation is a basic but important step in image processing. In some basic image processing works such as video analysis, video object recognition, etc., or some high level applications such as military surveillance, content-based video retrieval, etc., all the frames have to be segmented into meaningful parts at first. And then those parts can further be processed. MPEG-4 multimedia communication standard enables the content-based functionalities by using the video objects plane as the basic coding element. From the point of view of human vision system, video segmentation segments meaningful parts from the video stream that conform to what human vision feels. Because while seeing a scene by human naked eye, the scene is composed of many objects, not pixel by pixel. In this thesis, we will focus on the image/video segmentation and its applications. One of our goals in this thesis is to design and implement an image/video segmentation system based on existing methods, which are widely used in image/video segmentation nowadays. We decompose the system into several stages, each of which performs a specific task. Then, based on the output of each stage, we can refine the algorithms in that stage to obtain a better result. We can retrieve areas from image data which more accurately conform to what human vision system feels. In other words, we retrieve the moving part, say, foreground, from the static background. After obtaining the segmentation results, a compression algorithm such as MPEG-4 can be used to compress these retrieved regions, which is referred to as content-based coding. Besides, other image processing applications can be further developed. For example, remote surveillance and monitoring system can be developed for detecting the moving objects using the segmentation algorithms described in this thesis.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: video streaming, motion detection, adaptive scaling. Includes bibliographical references (p. 48-51).

No description available.

Thesis (M.F.A.)--Rochester Institute of Technology, 1990. / Typescript. Includes bibliographical references (leaf 22).

Thesis (M.F.A.)--Rochester Institute of Technology, 1995. / Typescript. Bibliography: leaf 17

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 86-90). Also available in electronic version.

Thesis (M.A.)--Ohio State University, 1985. / Includes vita. Includes bibliographical references (leaves 83-87). Available online via OhioLINK's ETD Center.

Cette thèse porte sur l’élaboration d’un système de manipulation de vidéo. De manière plus précise il s’agit d’extraction et de composition d’objets vidéo. Dans le domaine du traitement d’image fixe, les techniques d’extraction et de démélange (connus sous le nom de matting) et de composition ont vu une réelle amélioration au cours de la dernière décennie, surtout avec l’apparition de méthodes semi-automatiques profitant d’une interaction avec l’utilisateur pour surmonter le gap sémantique. Cela a permis d’aboutir à des algorithmes de plus en plus rapides et de plus en plus robustes. Dans le cadre du traitement de vidéo, cette problématique forme encore un très intéressant challenge, issu du caractère volumineux, en termes complexité de données et de nombre d’images dans la vidéo. Cet élément fait en sorte que la tâche accomplie par l’utilisateur pour marquer un objet d’intérêt peut être très fastidieuse ou souvent impossible. Les travaux que nous avons réalisés au cours de cette thèse se sont concentrés sur l’extension et l’adaptation de la transformée en distance et des courbes actives pour la propagation des marquages d’objets vidéo. Nous avons aussi proposé une amélioration d’une technique pouvant être utilisée avec ces marquages pour l’extraction d’objet vidéo.Dans le premier chapitre nous présentons le contexte et la problématique de nos travaux. Dans le deuxième chapitre nous faisons un tour d’horizon des approches, des outils d’édition de vidéo existant sur le marché, tout en les classant en deux familles : édition par morceaux ou par blocs et édition par objets vidéo. Ensuite, nous présentons un rapide état de l’art sur la segmentation que nous décomposons en trois parties : la segmentation classique, la segmentation interactive et l’image matting. Aussi nous détaillons l’extension de l’image matting au video matting en présentant les principales approches existantes. Le chapitre 3 présente notre première approche pour la propagation de marquage dans les vidéos. Cette approche est une approche volumique 2D+T tirant sa puissance de ce que nous avons bâti une CDT (transformée en distance couleur). Le chapitre 4, lui, présente notre évolution de perception vers un processus de propagation de marquages plus robuste et plus performant basé sur les courbes actives. Nous commençons par faire un état de l’art abrégé sur les courbes actives et nous présentons par la suite notre modélisation et son application. Nous détaillons, aussi le mécanisme de gestion dynamique des poids que nous avons mis en place. Dans le chapitre 5, nous allons discuter de l’application de notre système pour le matting vidéo et nous présentons les améliorations que nous avons apportés à l’approche Spectral Matting, dans ce but / Pas de résumé en anglais