Entropy-constrained vector quantization of images in the transform domain

Jong S. Lee, Rin C. Kim, Sang U. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Number of pages12
Editionp 1
StatePublished - 1994
EventVisual Communications and Image Processing '94 - Chicago, IL, USA
Duration: 25 Sep 199429 Sep 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Numberp 1
ISSN (Print)0277-786X


ConferenceVisual Communications and Image Processing '94
CityChicago, IL, USA


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