Perceptually based directional classified gain-shape vector quantization

Abstract

A new image coding system, termed directional classified gain-shape vector quantization (DCGSVQ), is introduced in this paper. A content-classifier, operating in the spatial domain, is employed to classify each image block of 8/spl times/8 pixels into one of several classes which represent various image patterns (edges in various directions, monotone areas, complex texture, etc.). Then a classified gain-shape vector quantizer is employed in the cosine domain to encode directional vectors of AC transform coefficients while using either a scalar quantizer or a gain-shape vector quantizer to encode the DC coefficients. A new vector configuration scheme is proposed in order to better adapt the system to the local statistics of the image blocks. In addition, various properties of the human visual system like frequency sensitivity, the masking effect, and orientation sensitivity are incorporated into the proposed system to improve further the subjective quality of the reconstructed images. A new algorithm for designing the various shape codebooks, needed for the DCGSVQ, is proposed based on the classified nearest neighbor clustering (CNNC) algorithm of Kubrick and Ellis (1990). Finally, an optional simple method for feature enhancement, based on inherent properties of the proposed system, is proposed enabling further image processing at the receiver. Coding results are presented showing a very good subjective quality of reconstructed images for bit-rates in the range 0.48-0.625 b per pixel. >