A Transform Domain Classified Vector Quantizer for Image Coding

An image-coding technique, in which the discrete cosine transform (DCT) Is combined with a classified vector quantization (CVQ), i.e., DCT-CVQ, is presented in this paper. In this scheme, a DCT-transformed Input block Is classified according to the perceptual feature and partitioned into several smaller vectors and then vector quantized. In our approach, an efficient edge-oriented classifier employing the DCT coefficients as feature for classification is used to maintain the edge Integrity in the reconstructed Image. Also, In this paper, 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 investigated. Because the employed distortion rate function (DRF) 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 VQ's at low bit rates, and the modification is shown to be a good agreement with experimental results. Simulation results indicate that a good visual quality of the coded image in the range of 0.4 similar to 0.1 b/pixel (bpp) is obtained.