1 November 1992 Adaptive entropy-constrained predictive vector quantization of image with a classifier and a variable vector dimension scheme
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Proceedings Volume 1818, Visual Communications and Image Processing '92; (1992) https://doi.org/10.1117/12.131464
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
Abstract
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 X 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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rin Chul Kim, Rin Chul Kim, Sang Uk Lee, Sang Uk Lee, } "Adaptive entropy-constrained predictive vector quantization of image with a classifier and a variable vector dimension scheme", Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131464; https://doi.org/10.1117/12.131464
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