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Proceedings Paper

Vector quantization of multiresolution morphological pyramids for efficient coding of images
Author(s): Zhiyang Zhang; Sunanda Mitra
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Paper Abstract

Morphological pyramid has been proven to be a useful tool in image compression due to low computational complexity, simple implementation and good compression performance based on minimization of entropy. Several morphology based pyramid decomposition techniques already exist. These techniques use morphological filters prior to the down sampling of images. The coding schemes developed commonly omit the first error image of the error pyramid to achieve high compression ratios. However, fine image details may be lost in this process. In order to get high quality lossy image, an estimator involving connectivity preserving filters for the first error image has been used. By using this estimator, the bits per pixel required to code the first error image can be reduced by 30 to 40 percent to obtain `near lossless' compression. In this paper, we apply variable Vector Quantization (VQ) to pyramid coding. We compare the performance of the above estimator to that of VQ scheme. For multi- level pyramid, we discuss accumulative errors and use a modified pyramid generation structure to reduce the accumulative errors. We perform our comparison on two standard images and use Peak Signal to Noise Ratio to judge the compression efficiency and visual quality.

Paper Details

Date Published: 3 October 1995
PDF: 11 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222726
Show Author Affiliations
Zhiyang Zhang, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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