Adaptive entropy-constrained predictive vector quantization of image with a classifier and a variable vector dimension scheme

In this paper, an entropy constrained predictive vector quantizer (ECPVQ) for image coding is described, and an adaptive ECPVQ (AECPVQ) technique to take into account the local characteristics of the input image is proposed. The adaptation is achieved by employing a classifier and the variable vector dimension scheme. In the proposed AECPVQ coder, separate predictors and codebooks are prepared for each class. The 6 × 6 input block is classified into one of the predetermined 6 classes according to the distribution of the feature vector in the DCT domain. Then, the input block is partitioned into several small vectors by the proposed variable vector dimension scheme to take into account the orientation of edge and the variances for each class. The vectors in each class are encoded using the corresponding codebook and the predictor. The computer simulation result shows that the proposed AECPVQ outperforms the conventional ECPVQ in terms of both the subjective quality and peak signal to noise ratio. For example, the AECPVQ enjoys a 1.5 dB gain over the ECPVQ at 0.7 bits/pel on the Lena image.