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Proceedings Paper

Entropy-constrained vector quantization of images in the transform domain
Author(s): Jong Seok Lee; Rin Chul Kim; Sang Uk Lee
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Paper Abstract

In this paper, two image coding techniques employing an entropy constrained vector quantizer (ECVQ) in the transform domain are presented. In both techniques, the transformed DCT coefficients are rearranged into the Mandala blocks for vector quantization. The first technique is based on the unstructured ECVQ designed separately for each Mandala block, while the second technique employs a structured ECVQ, i.e., an entropy constrained lattice vector quantizer (ECLVQ). In the ECLVQ, unlike the conventional lattice VQ combined with entropy coding, we take into account both the distortion and entropy in the encoding. Moreover, in order to improve the performance further, the ECLVQ parameters are optimized according to the input image statistics. Also we reduce the size of the variable word-length code table, by decomposing the lattice codeword into its magnitude and sign information. The performances of both techniques are evaluated on the real images, and it is found that the proposed techniques provide 1 - 2 dB gain over the DCT-classified VQ at bit rates in the range of 0.3 - 0.5 bits per pixel.

Paper Details

Date Published: 16 September 1994
PDF: 12 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185986
Show Author Affiliations
Jong Seok Lee, Seoul National Univ. (South Korea)
Rin Chul Kim, Seoul National Univ. (South Korea)
Sang Uk Lee, Seoul National Univ. (South Korea)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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