Entropy-constrained vector quantization of images in the transform domain

Jong Seok Lee; Rin Chul Kim; Sang Uk Lee

doi:10.1117/12.185986

16 September 1994 Entropy-constrained vector quantization of images in the transform domain

Jong Seok Lee, Rin Chul Kim, Sang Uk Lee

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185986
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

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.

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Jong Seok Lee, Rin Chul Kim, and Sang Uk Lee "Entropy-constrained vector quantization of images in the transform domain", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185986

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KEYWORDS

Quantization

Distortion

Bridges

Computer programming

Image compression

Gold

Instrumentation control

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