Abstract
An image-coding technique, in which the discrete cosine transform (DCT) is combined with a classified vector quantization (CVQ), is presented. A DCT-transformed input block is classified according to the perceptual feature, partitioned into several smaller vectors, and then vector quantized. An efficient edge-oriented classifier employing the DCT coefficients as feature for classification is used to maintain the edge integrity in the reconstructed image. Based on a smaller geometric mean vector variance, a partition scheme in which 2-D DCT coefficients are divided into several smaller size vectors is also investigated. Because the distortion rate function (DRF) used is essential for the bit allocation algorithm to perform well, attempts have been made to modify the asymptotic DRF to estimate the performance of real VQs at low bit rates, and the modification is shown to be in good agreement with experimental results. Simulation results indicate that a good visual quality of the coded image in the range of 0.4 approximately 0.7 b/pixel is obtained. >
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