In this paper, we present an image compression scheme which exploits content-based quantization step considering the human visual system (HVS) in the Wavelet-Contourlet (DWT-CT) domain. Unlike usual DWT or CT algorithm, we decompose the image into DWT subbands followed by directional filters to account for the redundancy of the decomposition coefficients. We analyze the statistical property of the coefficients, and propose the adaptive quantization step according to the image content. Experimental results show that compared with the traditional EZW, the proposed scheme guarantees low compression ratio and distortion as well.

HVS-Based Image Compression Scheme in Wavelet-Contourlet Domain

Tunable-Q contourlet transform for image representation

Purpose – Because of advantages such as multi‐scale and multi‐direction, contourlet transform is better at treating a 2‐D image than wavelet transform and the Classified Hidden Markov Tree (CHMT) model is able to analyze statistical character of coefficients, which means they can be analyzed more efficiently and effectively. So in this paper, the purpose is to use contourlet transform and CHMT model to redevelop traditional set partitioning in hierarchical trees (SPIHT) code algorithm, and by using these two methods, hope to decrease the distortion and increase the quality.Design/methodology/approach – In this paper, the algorithm is divided into two parts: code part and decode part. As all processes are operated in the contourlet domain, contourlet transform is finished at the beginning of the code part. SPIHT algorithm in the contourlet domain will code these contourlet coefficients. CHMT model will be built in decode part, it will optimize decoded coefficients by calculating their statistical relations...

A static image coding algorithm based on contourlet Classified Hidden Markov Tree model

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A static image coding algorithm based on contourlet Classified Hidden Markov Tree model