A vector quantization scheme based on the classified vector quantization (CVQ) concept, called predictive classified vector quantization (PCVQ), is presented. Unlike CVQ where the classification information has to be transmitted, PCVQ predicts it, thus saving valuable bit rate. Two classifiers, one operating in the Hadamard domain and the other in the spatial domain, were designed and tested. The classification information was predicted in the spatial domain. The PCVQ schemes achieved bit rate reductions over the CVQ ranging from 20 to 32% for two commonly used color test images while maintaining the same acceptable image quality. Bit rates of 0.70-0.93 bits per pixel (bpp) were obtained depending on the image and PCVQ scheme used.
This paper describes a hybrid image coding scheme called Classified Hybrid Image Coder (CHIC). CHIC combines t h e Classified Vector Quantization (CVQ) and t h e Discrete Cosine Transform (DCT) coding schemes in order t o achieve a better performance t h a n would be possible if they were t o be used separately.The image is sub-divided into 16x16 pixel blocks(vectors) and transformed using DCT. Each vector is classified into a n edge vector and a shade vector. Classification is based on t h e distribution and value of the DCT coefficients. i n each vector. Shade vectors a r e coded using CVQ. Edge vectors a r e further classified according to their edge strengths. In each o f these classes, t h e coefficients a r e quantized and then coded with an adaptive arithmetic coder (AAC). The performance of t h e AAC is further improved by further classifying t h e DCT coefficients into different groups, based on their different frequency ranges. The AAC uses a s e t of training images t o generate the initial histogram tables.
IntroductionVector quantization (VQ), which was introduced
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