Image details appear as wavelet coefficients with large magnitude in the wavelet transform domain. Image compression methods such as the embedded zerotree wavelet encoding and the set partitioning in hierarchical trees select wavelet coefficients in the order of their significance (magnitude) and encode them generating an embedded bit stream. In existing wavelet based image compression techniques, the significance of a wavelet coefficient is solely defined by its magnitude. In this paper, we describe a flexible scheme to prioritize wavelet coefficients based on the features they exhibit. The proposed scheme combines tree based wavelet coefficient representation with the implicit transmission of data about image features that need to be emphasized. The experimental results presented in this paper demonstrate that it is possible to enhance the image features in the reconstructed images by embedding locally adaptive image processing techniques in the compression algorithm. The main advantage of the proposed technique over the existing methods is that it exploits the embedded zerotree data structure to eliminate the need to send side (additional) information to the decoder regarding the feature selection process.

Previous research advances have shown that wavelet-based image-compression techniques offer several advantages over traditional techniques in terms of progressive transmission capability, compression efficiency, and bandwidth utilization. The embedded zerotree wavelet (EZW) coding technique suggested by Shapiro (1992), and its modification-set partitioning in hierarchical trees (SPIHT), suggested by Said and Pearlman (19996)-demonstrate the competitive performance of wavelet-based compression schemes. The EZW-based lossless image coding framework consists of three stages: (1) reversible discrete wavelet transform; (2) hierarchical ordering and selection of wavelet coefficients; and (3) context-modeling-based entropy (arithmetic) coding. The performance of the compression algorithm depends on the choice of various parameters and the implementation strategies employed in all the three stages. This paper proposes different context modeling and selection techniques for efficient entropy encoding of wavelet coefficients, along with the modifications performed to the SPIHT algorithm. The results of several experiments presented in this paper demonstrate the importance of context modeling in the EZW framework. Furthermore, this paper shows that appropriate context modeling improves the performance of compression algorithm after a multilevel subband decomposition is performed.

Lossless compression of video is an important problem in multimedia applications such as telemedicine and satellite imagery. Hence a need arises to optimize storage and transmission bandwidth. In this paper, we present the several experiments conducted to compress each frame of a video sequence by optimizing different parameters in an EZW framework. Compression efficiencies for the football video sequence consisting of 60 frames are tabulated

The EZW lossless coding framework consists of three stages: (i) a reversible wavelet transform, (ii) an EZW data structure to order the coefficients and (iii) an arithmetic coding using context modeling. In this work, we discuss the various experiments conducted on context modeling of wavelet coefficients for arithmetic coding to optimize the compression efficiency. The context modeling of wavelet coefficients can be classified into two parts: (i) context modeling of significance information and (ii) context modeling of the remaining or residue information. It was observed from our experiments while context modeling of residue helped in achieving considerable compression efficiency, the context modeling of significance information helped only to a modest extent.

The framework for an image coding system based on embedded zerotrees consists of three stages: (i) wavelet transform (ii) embedded zerotree encoding and (iii) adaptive arithmetic encoding. In this framework, the selection of the wavelet filter becomes an important issue. In this paper, we present a modification to the scanning approach in the set partitioning algorithm proposed in Said and Pearlman (1996) to exploit the correlation in a local neighborhood. Two new criteria are proposed for evaluating the performance of wavelets in lossless image compression applications: zero tree count and monotone spectral ordering of subbands produced after the wavelet transform in a multiresolution scheme. We evaluate several wavelet filters to test the evaluation criteria and present experimental results to justify the proposed performance criteria

Research advances in wavelet theory and subband coding have created a surge of interest in wavelet based applications during the past decade. Image coding (or compression) is an important application that has benefited significantly from the wavelet theory. Lossless image coding using the embedded zerotree wavelet (EZW) is the main focus of this work and also in the sequel to this work. The EZW lossless coding framework consists of three stages: (i) a reversible wavelet transform, (ii) an EZW data structure to order the coefficients and (iii) an arithmetic coding using context modeling. In this work, we discuss the experiments conducted in the first and second stage of the framework using the set partitioning based EZW coding to optimize the compression efficiency.

From a multiresolution perspective, a wavelet decomposition of an image f(x,y) at a resolution. 2/sup j/, consists of an approximated image at a resolution 2/sup j-1/ and three detail images along the horizontal, vertical and diagonal directions. In the first scheme, the approximated wavelet coefficients are encoded using variable block size segmentation (VBSS) algorithm and the detail signals are encoded using directional prediction and categorization. The residual error due to the finite precision arithmetic is significant and is encoded using adaptive arithmetic encoding technique. In the alternate scheme, we propose a new concept of multiresolution which avoids the finite precision arithmetic errors. The approximated image in the alternate scheme is a decimated version of the original image. The equivalence of the alternate multiresolution scheme to the original multiresolution scheme is also analyzed mathematically. The performance of scheme one is comparable to that exhibited by JPEG lossless schemes.

We propose a lossless image compression scheme using wavelet decomposition. Wavelet decomposition of an image f(x,y) at a resolution 2/sup j/ consists of an approximated image at a resolution 2/sup j-1/ and three detail images along the horizontal, vertical and diagonal directions. The approximated wavelet coefficients are encoded using a variable block size segmentation (VBSS) algorithm proposed by Ranganathan et.al. (see IEEE Trans. on Image Proc., vol.4, no.10,p.1396-1406, 1995) and the detail signals are encoded using directional prediction and categorization similar to that in the VBSSS algorithm. The residual error due to the finite precision arithmetic is encoded using adaptive arithmetic coding (AAC). The performance of the proposed approach is comparable to that exhibited by JPEG lossless schemes while being better than the Huffman, the Lempe-Ziv and arithmetic coding.