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Proceedings Paper

Model-based VQ for image data archival, retrieval, and distribution
Author(s): Mareboyana Manohar; James C. Tilton
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Paper Abstract

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of vector quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and human visual system (HVS) models. The error model assumed is the Laplacian distribution with mean, (lambda) , computed from a sample of the input image. A Laplacian distribution with mean, (lambda) , is generated with a uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produced the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, (lambda) , that is included in the coded file to repeat the codebook generation process for decoding.

Paper Details

Date Published: 16 June 1995
PDF: 9 pages
Proc. SPIE 2488, Visual Information Processing IV, (16 June 1995); doi: 10.1117/12.211974
Show Author Affiliations
Mareboyana Manohar, Bowie State Univ. (United States)
James C. Tilton, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 2488:
Visual Information Processing IV
Friedrich O. Huck; Richard D. Juday, Editor(s)

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