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Proceedings Paper

Low-bit-rate subband image coding with matching pursuits
Author(s): Hamid Rabiee; S. R. Safavian; Thomas R. Gardos; A. J. Mirani
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Paper Abstract

In this paper, a novel multiresolution algorithm for low bit-rate image compression is presented. High quality low bit-rate image compression is achieved by first decomposing the image into approximation and detail subimages with a shift-orthogonal multiresolution analysis. Then, at the coarsest resolution level, the coefficients of the transformation are encoded by an orthogonal matching pursuit algorithm with a wavelet packet dictionary. Our dictionary consists of convolutional splines of up to order two for the detail and approximation subbands. The intercorrelation between the various resolutions is then exploited by using the same bases from the dictionary to encode the coefficients of the finer resolution bands at the corresponding spatial locations. To further exploit the spatial correlation of the coefficients, the zero trees of wavelets (EZW) algorithm was used to identify the potential zero trees. The coefficients of the presentation are then quantized and arithmetic encoded at each resolution, and packed into a scalable bit stream structure. Our new algorithm is highly bit-rate scalable, and performs better than the segmentation based matching pursuit and EZW encoders at lower bit rates, based on subjective image quality and peak signal-to-noise ratio.

Paper Details

Date Published: 9 January 1998
PDF: 6 pages
Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998); doi: 10.1117/12.298399
Show Author Affiliations
Hamid Rabiee, Intel Corp. (United States)
S. R. Safavian, LCC Cellular Institute (United States)
Thomas R. Gardos, Intel Corp. (United States)
A. J. Mirani, Intel Corp. (United States)

Published in SPIE Proceedings Vol. 3309:
Visual Communications and Image Processing '98
Sarah A. Rajala; Majid Rabbani, Editor(s)

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