VLSI implementation of a massively parallel wavelet based zerotree coder for the intelligent pixel array

In the span of a few years, mobile multimedia communication has rapidly become a significant area of research and development constantly challenging boundaries on a variety of technologic fronts. Mobile video communications in particular encompasses a number of technical hurdles that generally steer technological advancements towards devices that are low in complexity, low in power usage yet perform the given task efficiently. Devices of this nature have been made available through the use of massively parallel processing arrays such as the Intelligent Pixel Processing Array. The Intelligent Pixel Processing array is a novel concept that integrates a parallel image capture mechanism, a parallel processing component and a parallel display component into a single chip solution geared toward mobile communications environments, be it a PDA based system or the video communicator wristwatch portrayed in Dick Tracy episodes. This thesis details work performed to provide an efficient, low power, low complexity solution surrounding the massively parallel implementation of a zerotree entropy codec for the Intelligent Pixel Array

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Improving Embedded Image Coding Using Zero Block - Quad Tree

The traditional multi-bitstream approach to the heterogeneity issue is very constrained and inefficient under multi bit rate applications. The multi bitstream coding techniques allow partial decoding at a various resolution and quality levels. Several scalable coding algorithms have been proposed in the international standards over the past decade, but these former methods can only accommodate relatively limited decoding properties. To achieve efficient coding during image coding the multi resolution compression technique is been used. To exploit the multi resolution effect of image, wavelet transformations are devolved. Wavelet transformation decompose the image coefficients into their fundamental resolution, but the transformed coefficients are observed to be non-integer values resulting in variable bit stream. This transformation result in constraint bit rate application with slower operation. To overcome stated limitation, hierarchical tree based coding were implemented which exploit the relation between the wavelet scale levels and generate the code stream for transmission

A new multistage lattice vector quantization with adaptive subband thresholding for image compression

Lattice vector quantization (LVQ) reduces coding complexity and computation due to its regular structure. A new multistage LVQ (MLVQ) using an adaptive subband thresholding technique is presented and applied to image compression. The technique concentrates on reducing the quantization error of the quantized vectors by "blowing out" the residual quantization errors with an LVQ scale factor. The significant coefficients of each subband are identified using an optimum adaptive thresholding scheme for each subband. A variable length coding procedure using Golomb codes is used to compress the codebook index which produces a very efficient and fast technique for entropy coding. Experimental results using the MLVQ are shown to be significantly better than JPEG 2000 and the recent VQ techniques for various test images

Reliable and Efficient coding Technique for Compression of Medical Images based on Region of Interest using Directional Filter Banks

Medical images carry huge and vital information. It is necessary to compress the medical images without losing its vital-ness. The proposed algorithm presents a new coding technique based on  image compression using contourlet transform used in different modalities of medical imaging. Recent reports on natural image compression have shown superior performance of contourlet transform, a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. As far as medical images are concerned the diagnosis part (ROI) is of much important compared to other regions. Therefore those portions are segmented from the whole image using  fuzzy C-means(FCM) clustering technique. Contourlet transform is then applied to ROI portion which performs Laplacian Pyramid(LP) and Directional Filter Banks. The region of less significance are compressed using Discrete Wavelet Transform and finally modified embedded zerotree wavelet algorithm is applied which uses six symbols instead of four symbols used in Shapiro’s EZW to the resultant image which shows better PSNR and high compression ratio.Â

A new multistage lattice vector quantization with adaptive subband thresholding for image compression

Lattice vector quantization (LVQ) reduces coding complexity and computation due to its regular structure. A new multistage LVQ (MLVQ) using an adaptive subband thresholding technique is presented and applied to image compression. The technique concentrates on reducing the quantization error of the quantized vectors by "blowing out" the residual quantization errors with an LVQ scale factor. The significant coefficients of each subband are identified using an optimum adaptive thresholding scheme for each subband. A variable length coding procedure using Golomb codes is used to compress the codebook index which produces a very efficient and fast technique for entropy coding. Experimental results using the MLVQ are shown to be significantly better than JPEG 2000 and the recent VQ techniques for various test images

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