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Proceedings Paper

Wavelet coding of images using trellis-coded quantization
Author(s): Parthasarathy Sriram; Michael W. Marcellin
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Paper Abstract

The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multiresolution approximations. An image is decomposed into a sequence of orthogonal components, the first being an approximation of the original image at some 'base' resolution. By the addition of successive (orthogonal) 'error' images, approximations of higher resolution are obtained. Trellis coded quantization (TCQ) is known as an effective scheme for quantizing memoryless sources with low to moderate complexity. The TCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. In this work, we investigate the use of entropy-constrained TCQ for encoding wavelet coefficients at different bit rates. The lowest-resolution sub-image is quantized using a 2-D discrete cosine transform encoder. For encoding the 512 X 512, 8- bit, monochrome 'Lenna' image, a PSNR of 39.00 dB is obtained at an average bit rate of 0.89 bits/pixel.

Paper Details

Date Published: 1 October 1992
PDF: 10 pages
Proc. SPIE 1705, Visual Information Processing, (1 October 1992); doi: 10.1117/12.138469
Show Author Affiliations
Parthasarathy Sriram, Univ. of Arizona (United States)
Michael W. Marcellin, Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 1705:
Visual Information Processing
Friedrich O. Huck; Richard D. Juday, Editor(s)

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