Lossy Image Compression Using Wavelets

Authors:
Nick D. Panagiotacopulos;Ken Friesen;Sukit Lertsuntivit
Affiliations:
Department of Electrical Engineering, College of Engineering, California State University, Long Beach, CA 90840, U.S.A.;Department of Electrical Engineering, College of Engineering, California State University, Long Beach, CA 90840, U.S.A.;Department of Electrical Engineering, College of Engineering, California State University, Long Beach, CA 90840, U.S.A.
Venue:
Journal of Intelligent and Robotic Systems
Year:
2000

Citing 5
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Abstract

In this paper, we report the results of the application of transform coding image data compression techniques using Daubechies and Coifman wavelets. More specifically, D2, D4, D8, D16, and C6, C12 wavelets were used. The results from these wavelets were compared with those of discrete cosine transform. They clearly demonstrated the superiority of the wavelet-based techniques both in compression ratios and image quality, as well as in computational speed. Two quantization methods were used: non-uniform scalar quantization and pseudo-quantization. Both produced satisfactory results (86–88% compression ratio, and acceptable image quality).