Non-expansive symmetrically extended wavelet transform for arbitrarily shaped video object plane.

by Lai Chun Kit.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 68-70).Abstract also in Chinese.ACKNOWLEDGMENTS --- p.IVABSTRACT --- p.vChapter Chapter 1 --- Traditional Image and Video Coding --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Fundamental Principle of Compression --- p.1Chapter 1.3 --- Entropy - Value of Information --- p.2Chapter 1.4 --- Performance Measure --- p.3Chapter 1.5 --- Image Coding Overview --- p.4Chapter 1.5.1 --- Digital Image Formation --- p.4Chapter 1.5.2 --- Needs of Image Compression --- p.4Chapter 1.5.3 --- Classification of Image Compression --- p.5Chapter 1.5.4 --- Transform Coding --- p.6Chapter 1.6 --- Video Coding Overview --- p.8Chapter Chapter 2 --- Discrete Wavelets Transform (DWT) and Subband Coding --- p.11Chapter 2.1 --- Subband Coding --- p.11Chapter 2.1.1 --- Introduction --- p.11Chapter 2.1.2 --- Quadrature Mirror Filters (QMFs) --- p.12Chapter 2.1.3 --- Subband Coding for Image --- p.13Chapter 2.2 --- Discrete Wavelets Transformation (DWT) --- p.15Chapter 2.2.1 --- Introduction --- p.15Chapter 2.2.2 --- Wavelet Theory --- p.15Chapter 2.2.3 --- Comparison Between Fourier Transform and Wavelet Transform --- p.16Chapter Chapter 3 --- Non-expansive Symmetric Extension --- p.19Chapter 3.1 --- Introduction --- p.19Chapter 3.2 --- Types of extension scheme --- p.19Chapter 3.3 --- Non-expansive Symmetric Extension and Symmetric Sub-sampling --- p.21Chapter Chapter 4 --- Content-based Video Coding in MPEG-4 Purposed Standard --- p.24Chapter 4.1 --- Introduction --- p.24Chapter 4.2 --- Motivation of the new MPEG-4 standard --- p.25Chapter 4.2.1 --- Changes in the production of audio-visual material --- p.25Chapter 4.2.2 --- Changes in the consumption of multimedia information --- p.25Chapter 4.2.3 --- Reuse of audio-visual material --- p.26Chapter 4.2.4 --- Changes in mode of implementation --- p.26Chapter 4.3 --- Objective of MPEG-4 standard --- p.27Chapter 4.4 --- Technical Description of MPEG-4 --- p.28Chapter 4.4.1 --- Overview of MPEG-4 coding system --- p.28Chapter 4.4.2 --- Shape Coding --- p.29Chapter 4.4.3 --- Shape Adaptive Texture Coding --- p.33Chapter 4.4.4 --- Motion Estimation and Compensation (ME/MC) --- p.35Chapter Chapter 5 --- Shape Adaptive Wavelet Transformation Coding Scheme (SA WT) --- p.36Chapter 5.1 --- Shape Adaptive Wavelet Transformation --- p.36Chapter 5.1.1 --- Introduction --- p.36Chapter 5.1.2 --- Description of Transformation Scheme --- p.37Chapter 5.2 --- Quantization --- p.40Chapter 5.3 --- Entropy Coding --- p.42Chapter 5.3.1 --- Introduction --- p.42Chapter 5.3.2 --- Stack Run Algorithm --- p.42Chapter 5.3.3 --- ZeroTree Entropy (ZTE) Coding Algorithm --- p.45Chapter 5.4 --- Binary Shape Coding --- p.49Chapter Chapter 6 --- Simulation --- p.51Chapter 6.1 --- Introduction --- p.51Chapter 6.2 --- SSAWT-Stack Run --- p.52Chapter 6.3 --- SSAWT-ZTR --- p.53Chapter 6.4 --- Simulation Results --- p.55Chapter 6.4.1 --- SSAWT - STACK --- p.55Chapter 6.4.2 --- SSAWT ´ؤ ZTE --- p.56Chapter 6.4.3 --- Comparison Result - Cjpeg and Wave03. --- p.57Chapter 6.5 --- Shape Coding Result --- p.61Chapter 6.6 --- Analysis --- p.63Chapter Chapter 7 --- Conclusion --- p.64Appendix A: Image Segmentation --- p.65Reference --- p.6

Active mesh coding and rate-distortion theory

This paper presents a video coding scheme for very low bit rate applications. The coding approach relies on active meshes and can be viewed as a particular case of region-based coding. The active mesh is used to efficiently represent and code the various regions of the scene and the motion information. The variation of the mesh topology as well as the strategy for coding the synthesis error are defined by an optimization technique following the rate-distortion criterion.Peer ReviewedPostprint (published version

An efficient algorithm for realizing matching pursuits and its applications in MPEG4 coding system

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2000-2001 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

High-performance compression of visual information - A tutorial review - Part I : Still Pictures

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must be defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare stateof- the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated

Steered mixture-of-experts for light field images and video : representation and coding

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution

Spline-based medial axis transform representation of binary images

Medial axes are well-known descriptors used for representing, manipulating, and compressing binary images. In this paper, we present a full pipeline for computing a stable and accurate piece-wise B-spline representation of Medial Axis Transforms (MATs) of binary images. A comprehensive evaluation on a benchmark shows that our method, called Spline-based Medial Axis Transform (SMAT), achieves very high compression ratios while keeping quality high. Compared with the regular MAT representation, the SMAT yields a much higher compression ratio at the cost of a slightly lower image quality. We illustrate our approach on a multi-scale SMAT representation, generating super-resolution images, and free-form binary image deformation