Yim, Ka Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 43). / Abstracts in English and Chinese. / Acknowledgements --- p.vii / TABLE OF CONTENTS --- p.viii / LIST OF TABLES --- p.x / LIST OF FIGURES --- p.xi / Chapter CHAPTER 1 : --- INTRODUCTION --- p.1 / Chapter 1.1. --- Motivation --- p.1 / Chapter 1.2. --- Overview --- p.2 / Chapter 1.3. --- H.264 Overview --- p.2 / Chapter CHAPTER 2 : --- CABAC --- p.7 / Chapter 2.1. --- Introduction --- p.7 / Chapter 2.2. --- CABAC Decoder Implementation Review --- p.7 / Chapter 2.3. --- CABAC Algorithm Review --- p.9 / Chapter 2.4. --- Proposed CABAC Decoder Implementation --- p.13 / Chapter 2.5. --- FSM Method Bin Matching --- p.20 / Chapter 2.6. --- CABAC Experimental Results --- p.22 / Chapter 2.7. --- Summary --- p.26 / Chapter CHAPTER 3 : --- INTEGRATION --- p.27 / Chapter 3.1. --- Introduction --- p.27 / Chapter 3.2. --- Reused Baseline Decoder Review --- p.27 / Chapter 3.3. --- Integration --- p.30 / Chapter 3.4. --- Proposed Solution for Motion Vector Decoding --- p.33 / Chapter 3.5. --- Synthesis Result and Performance Analysis --- p.37 / Chapter CHAPTER 4 : --- CONCLUSION --- p.39 / Chapter 4.1. --- Main Contribution --- p.39 / Chapter 4.2. --- Reflection on the Development --- p.39 / Chapter 4.3. --- Future Work --- p.41 / BIBLIOGRAPHY --- p.43

Bo Yan. / "May 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 117-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

No description available.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: MPEG; video; sensor. Includes bibliographical references (p.61-64).

<p>A transportation plan is a byproduct from the calculation of the Kantorovich distance between two images. It describes a transformation from one of the images to the other. This master thesis shows how transportation plans can be used for video coding and how to process the transportation plans to achieve a good bitrate/quality ratio. Various parameters are evaluated using an implemented transportation plan video coder.</p><p>The introduction of transform coding with DCT proves to be very useful, as it reduces the size of the resulting transportation plans. DCT coding roughly gives a 10-fold decrease in bitrate with maintained quality compared to the nontransformed transportation plan coding.</p><p>With the best settings for transportation plan coding, I was able to code a test sequence at about 5 times the bitrate for MPEG coding of the same sequence with similar quality.</p><p>As video coding using transportation plans is a very new concept, the thesis is ended with conclusions on the test results and suggestions for future research in this area.</p>

Scalable image and video has been proposed to transmit image and video signals over lossy networks, such as the Internet and wireless networks. However, scalability alone is not a complete solution since there is a conflict between the unequal importance of the scalable bit stream and the agnostic nature of packet losses in the network. This thesis investigates three methods to combat the detrimental effects of random packet losses to scalable images and video, namely the error resilient method, the error concealment method, and the unequal error protection method within the joint source-channel coding framework. For the error resilient method, an optimal bit allocation algorithm is proposed without considering the distortion caused by packet losses. The allocation algorithm is then extended to accommodate packet losses. For the error concealment method, a simple temporal error concealment mechanism is designed to work for video signals. For the unequal error protection method, the optimal protection allocation problem is formulated and solved. These methods are tested on the wavelet-based Set Partitioning in Hierarchical Trees(SPIHT) scalable image coder. Performance gains and losses in lossy and lossless environments are studied for both the original coder and the error-controlled coders. The results show performance advantages of all three methods over the original SPIHT coder. Particularly, the unequal error protection method and error concealment method are promising for future Internet/wireless image and video transmission, because the former has very good performance even at heavy packet loss (a PSNR of 22.00 dB has been seen at nearly 60% packet loss) and the latter does not introduce any extra overhead.

Scalable image and video has been proposed to transmit image and video signals over lossy networks, such as the Internet and wireless networks. However, scalability alone is not a complete solution since there is a conflict between the unequal importance of the scalable bit stream and the agnostic nature of packet losses in the network. This thesis investigates three methods to combat the detrimental effects of random packet losses to scalable images and video, namely the error resilient method, the error concealment method, and the unequal error protection method within the joint source-channel coding framework. For the error resilient method, an optimal bit allocation algorithm is proposed without considering the distortion caused by packet losses. The allocation algorithm is then extended to accommodate packet losses. For the error concealment method, a simple temporal error concealment mechanism is designed to work for video signals. For the unequal error protection method, the optimal protection allocation problem is formulated and solved. These methods are tested on the wavelet-based Set Partitioning in Hierarchical Trees(SPIHT) scalable image coder. Performance gains and losses in lossy and lossless environments are studied for both the original coder and the error-controlled coders. The results show performance advantages of all three methods over the original SPIHT coder. Particularly, the unequal error protection method and error concealment method are promising for future Internet/wireless image and video transmission, because the former has very good performance even at heavy packet loss (a PSNR of 22.00 dB has been seen at nearly 60% packet loss) and the latter does not introduce any extra overhead.

Due to the lossy nature of image/video compression and the expensive bandwidth and computation resources in a multimedia system, one of the key design issues for image and video coding/transcoding is to optimize trade-off among distortion, rate, and/or complexity. This thesis studies the application of rate distortion (RD) optimization approaches to image and video coding/transcoding for exploring the best RD performance of a video codec compatible to the newest video coding standard H.264 and for designing computationally efficient down-sampling algorithms with high visual fidelity in the discrete Cosine transform (DCT) domain. RD optimization for video coding in this thesis considers two objectives, i.e., to achieve the best encoding efficiency in terms of minimizing the actual RD cost and to maintain decoding compatibility with the newest video coding standard H.264. By the actual RD cost, we mean a cost based on the final reconstruction error and the entire coding rate. Specifically, an operational RD method is proposed based on a soft decision quantization (SDQ) mechanism, which has its root in a fundamental RD theoretic study on fixed-slope lossy data compression. Using SDQ instead of hard decision quantization, we establish a general framework in which motion prediction, quantization, and entropy coding in a hybrid video coding scheme such as H.264 are jointly designed to minimize the actual RD cost on a frame basis. The proposed framework is applicable to optimize any hybrid video coding scheme, provided that specific algorithms are designed corresponding to coding syntaxes of a given standard codec, so as to maintain compatibility with the standard. Corresponding to the baseline profile syntaxes and the main profile syntaxes of H.264, respectively, we have proposed three RD algorithms---a graph-based algorithm for SDQ given motion prediction and quantization step sizes, an algorithm for residual coding optimization given motion prediction, and an iterative overall algorithm for jointly optimizing motion prediction, quantization, and entropy coding---with them embedded in the indicated order. Among the three algorithms, the SDQ design is the core, which is developed based on a given entropy coding method. Specifically, two SDQ algorithms have been developed based on the context adaptive variable length coding (CAVLC) in H.264 baseline profile and the context adaptive binary arithmetic coding (CABAC) in H.264 main profile, respectively. Experimental results for the H.264 baseline codec optimization show that for a set of typical testing sequences, the proposed RD method for H.264 baseline coding achieves a better trade-off between rate and distortion, i.e., 12\% rate reduction on average at the same distortion (ranging from 30dB to 38dB by PSNR) when compared with the RD optimization method implemented in H.264 baseline reference codec. Experimental results for optimizing H.264 main profile coding with CABAC show 10\% rate reduction over a main profile reference codec using CABAC, which also suggests 20\% rate reduction over the RD optimization method implemented in H.264 baseline reference codec, leading to our claim of having developed the best codec in terms of RD performance, while maintaining the compatibility with H.264. By investigating trade-off between distortion and complexity, we have also proposed a designing framework for image/video transcoding with spatial resolution reduction, i.e., to down-sample compressed images/video with an arbitrary ratio in the DCT domain. First, we derive a set of DCT-domain down-sampling methods, which can be represented by a linear transform with double-sided matrix multiplication (LTDS) in the DCT domain. Then, for a pre-selected pixel-domain down-sampling method, we formulate an optimization problem for finding an LTDS to approximate the given pixel-domain method to achieve the best trade-off between visual quality and computational complexity. The problem is then solved by modeling an LTDS with a multi-layer perceptron network and using a structural learning with forgetting algorithm for training the network. Finally, by selecting a pixel-domain reference method with the popular Butterworth lowpass filtering and cubic B-spline interpolation, the proposed framework discovers an LTDS with better visual quality and lower computational complexity when compared with state-of-the-art methods in the literature.

Due to the lossy nature of image/video compression and the expensive bandwidth and computation resources in a multimedia system, one of the key design issues for image and video coding/transcoding is to optimize trade-off among distortion, rate, and/or complexity. This thesis studies the application of rate distortion (RD) optimization approaches to image and video coding/transcoding for exploring the best RD performance of a video codec compatible to the newest video coding standard H.264 and for designing computationally efficient down-sampling algorithms with high visual fidelity in the discrete Cosine transform (DCT) domain. RD optimization for video coding in this thesis considers two objectives, i.e., to achieve the best encoding efficiency in terms of minimizing the actual RD cost and to maintain decoding compatibility with the newest video coding standard H.264. By the actual RD cost, we mean a cost based on the final reconstruction error and the entire coding rate. Specifically, an operational RD method is proposed based on a soft decision quantization (SDQ) mechanism, which has its root in a fundamental RD theoretic study on fixed-slope lossy data compression. Using SDQ instead of hard decision quantization, we establish a general framework in which motion prediction, quantization, and entropy coding in a hybrid video coding scheme such as H.264 are jointly designed to minimize the actual RD cost on a frame basis. The proposed framework is applicable to optimize any hybrid video coding scheme, provided that specific algorithms are designed corresponding to coding syntaxes of a given standard codec, so as to maintain compatibility with the standard. Corresponding to the baseline profile syntaxes and the main profile syntaxes of H.264, respectively, we have proposed three RD algorithms---a graph-based algorithm for SDQ given motion prediction and quantization step sizes, an algorithm for residual coding optimization given motion prediction, and an iterative overall algorithm for jointly optimizing motion prediction, quantization, and entropy coding---with them embedded in the indicated order. Among the three algorithms, the SDQ design is the core, which is developed based on a given entropy coding method. Specifically, two SDQ algorithms have been developed based on the context adaptive variable length coding (CAVLC) in H.264 baseline profile and the context adaptive binary arithmetic coding (CABAC) in H.264 main profile, respectively. Experimental results for the H.264 baseline codec optimization show that for a set of typical testing sequences, the proposed RD method for H.264 baseline coding achieves a better trade-off between rate and distortion, i.e., 12\% rate reduction on average at the same distortion (ranging from 30dB to 38dB by PSNR) when compared with the RD optimization method implemented in H.264 baseline reference codec. Experimental results for optimizing H.264 main profile coding with CABAC show 10\% rate reduction over a main profile reference codec using CABAC, which also suggests 20\% rate reduction over the RD optimization method implemented in H.264 baseline reference codec, leading to our claim of having developed the best codec in terms of RD performance, while maintaining the compatibility with H.264. By investigating trade-off between distortion and complexity, we have also proposed a designing framework for image/video transcoding with spatial resolution reduction, i.e., to down-sample compressed images/video with an arbitrary ratio in the DCT domain. First, we derive a set of DCT-domain down-sampling methods, which can be represented by a linear transform with double-sided matrix multiplication (LTDS) in the DCT domain. Then, for a pre-selected pixel-domain down-sampling method, we formulate an optimization problem for finding an LTDS to approximate the given pixel-domain method to achieve the best trade-off between visual quality and computational complexity. The problem is then solved by modeling an LTDS with a multi-layer perceptron network and using a structural learning with forgetting algorithm for training the network. Finally, by selecting a pixel-domain reference method with the popular Butterworth lowpass filtering and cubic B-spline interpolation, the proposed framework discovers an LTDS with better visual quality and lower computational complexity when compared with state-of-the-art methods in the literature.

This paper proposes new approaches to improve the coding performance of intra block coding in H.264/AVC via finite state machine and residual prediction. Grounding on high correlation between neighboring blocks, finite state machine is employed both at encoder and decoder to reduce the number of bits required for encoding to enhance coding performance. Two extra intra prediction modes are created in our proposed method. Through these two modes, the number of bits required to denote the current block is greatly reduced and low bit rate can be achieved. According to spatial correlation, intra-coded residual prediction reduces residual block by neighboring residual block. In this paper, we combine finite state machine with intra-coded residual prediction to achieve better coding performance. Experimental results show that the proposed method can greatly improve coding efficiency of intra macroblock coding in H.264/AVC.