My research interests are in image processing, including lossless and lossy image compression, perceptually lossless image compression, and texture segmentation. Recently my research focus is on context-based conditional entropy coding of Vector Quantization(VQ) indexes for images. This method may open the new way to approach the rate-distortion optimality for traditional VQ design.

1. Bayesian-based Conditional Entropy Coding of VQ Indexes.

Based on source coding theory, Vector Quantization can approach the optimal rate-distortion tradeoff as the vector dimension approaches infinity. However, limited by the inherent computational intractability of VQ in dimensions, VQ's theoretical promise is yet to be fully realized in image compression practice because of its modest vector dimension. This must exist a large statistical redundancy between the inter blocks. Other than the general VQ point of view to utilize this property, we propose a new mothed which directly uses lossless techniques to code the VQ indexes obtaining from basic VQ. Bayesian rule is utilized to estimate the conditional probabilities of VQ indexes based on causal context in order to drive the arithmetic entropy coder, and the compression performance of this method can compare with the best VQ performance we have known.

2. Conditional Entropy Coding of VQ Indexes via Context Selection and Quantization.

Since the block sizes of practical VQ image coders are not large enough to exploit all high-order statistical dependencies among pixels, high-order statistical modeling of VQ indexes is presented. Through various context selection and quantization, one context structure which the pixels are contiguous to the current block is finally chosen. Based on such context, the conditional probabilities of VQ indexes can be estimated so good that the compression gains of this method are 10% higher than Address VQ(A-VQ), which first used lossless idea to code VQ index, with the same compression distortion and a tiny fraction of A-VQ's computational cost are needed.
This paper is under writing.

3. The Application of Conditional Entropy Coding of VQ Indexes for High-fidelity Medical Image Compression.

It's well known that VQ is very suitable for such kind of image compression that the images we want to compress belong to one category, such as medical image compression. However, low compression ratio impedes its development. Based on the encouraging results from conditional entropy coding of VQ index, we tailor this idea to medical images by refining the context modeling for a given class of medical images. This image-type-dependent method has obtained compression performance superior to a state-of-art wavelet image coder on the class of MR brain images.