This paper presents a novel wavelet compression technique to increase compression of images. Based on zerotree entropy coding method, this technique initially uses only two symbols (significant and zerotree) to compress image data for each level. Additionally, sign bit is used for newly significant coefficients to indicate them being positive or negative. Contrary to isolated zero symbols used in conventional zerotree algorithms, the proposed algorithm changes them to significant coefficients and saves its location, they are then treated just like other significant coefficients. This is done to decrease number of symbols and hence, decrease number of bits to represent the symbols used. In the end, algorithm indicates isolated zero coordinates that are used to change the value back to original during reconstruction. Noticeably high compression ratio is achieved for most of the images, without changing image quality.

A wavelet-based vector quantization scheme for image compression is introduced here. The proposed scheme obtains a better compression efficiency by the following three methods. (1) Utilizing the correlation among wavelet coefficients. (2) Placing different emphasis on wavelet coefficients at different levels. (3) Preserving the most important information of the image. In our experiments, simulation results show that this technique outperforms the recent SMVQ-ABC [1] and WTC-NIVQ [2] techniques.

Wavelet based image compression is getting popular due to its promising compaction properties at low bitrate. Zerotree wavelet image coding scheme efficiently exploits multi-level redundancy present in transformed data to minimize coding bits. In this paper, a new technique is proposed to achieve high compression by adding new zerotree and significant symbols to original EZW coder. Contrary to four symbols present in basic EZW scheme, the modified algorithm uses eight symbols to generate fewer bits for a given data. Subordinate pass of EZW is eliminated and replaced with fixed residual value transmission for easy implementation. This modification simplifies the coding technique as well and speeds up the process, retaining the property of embeddedness.

We introduce a new wavelet image coding framework using context-based zerotree quantization, where an unique and efficient method for optimization of zerotree quantization is proposed. Because of the localization properties of wavelets, when a wavelet coefficient is to be quantized, the best quantizer is expected to be designed to match the statistics of the wavelet coefficients in its neighborhood, that is, the quantizer should be adaptive both in space and frequency domain. Previous image coders tended to design quantizers in a band or a class level, which limited their performances as it is difficult for the localization properties of wavelets to be exploited. Contrasting with previous coders, we propose to trace the localization properties with the combination of the tree-structured wavelet representations and adaptive models which are spatial-varying according to the local statistics. In the paper, we describe the proposed coding algorithm, where the spatial-varying models are estimated from the quantized causal neighborhoods and the zerotree pruning is based on the Lagrangian cost that can be evaluated from the statistics nearby the tree. In this way, optimization of zerotree quantization is no longer a joint optimization problem as in SFQ. Simulation results demonstrate that the coding performance is competitive, and sometimes is superior to the best results of zerotree-based coding reported in SFQ.

We introduce an extended EZW coder that uses flexible zerotree coding of wavelet coefficients. A flexible parent-child relationship is defined so as to exploit spatial dependencies within a subband as well as hierarchical dependencies among multi-scale subbands. The new relationship is based on a particular statistics that a large coefficient is more likely to have large coefficients in its neighborhood in terms of space and scale. In the flexible relationship, a parent coefficient in a subband relates to four child coefficients in the next finer subband in the same orientation. If each of the children is larger than a given threshold, the parent extends its parentship to the neighbors close to its conventional children. A probing bit is introduced to indicate whether a significant parent has significant children to be scanned. This enables us to avoid excessive scan of insignificant coefficients. Also, produced symbols are re-symbolized into simple variable-length binary codes to remove some redundancy according to a pre-defined rule. As a result, the wavelet coefficients can be described with a small number of binary symbols. This binary symbol stream gives a competitive performance without an additional entropy coding and thus a fast encoding/decoding is possible. Moreover, the binary symbols can be more compressed by an adaptive arithmetic coding. Our experimental results are given in both binary-coded mode and arithmetic-coded mode. Also, these results are compared with those of the EZW coder.

A zerotree image-coding scheme is introduced that effectively exploits the inter-scale self-similarities found in the octave decomposition by a wavelet transform. A zerotree is useful for efficiently coding wavelet coefficients; its efficiency was proved by Shapiro's EZW. In the EZW coder, wavelet coefficients are symbolized, then entropy-coded for further compression. In this paper, we analyze the symbols produced by the EZW coder and discuss the entropy for a symbol. We modify the procedure used for symbol-stream generation to produce lower entropy. First, we modify the fixed relation between a parent and children used in the EZW coder to raise the probability that a significant parent has significant children. The modified relation is flexibly modified again based on the observation that a significant coefficient is more likely to have significant coefficients in its neighborhood. The three relations are compared in terms of the number of symbols they produce.