Paper
3 March 1995 Post-wavelet-transform redundancy and its reduction techniques for image compression
Author Affiliations +
Proceedings Volume 2418, Still-Image Compression; (1995) https://doi.org/10.1117/12.204126
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States
Abstract
This paper exploits three correlation patterns that exist in the discrete wavelet transform (DWT) coefficients of an decomposed image. DWT is known as a very useful transform for image compression. Since the correlation patterns are among the DWT coefficients, they are post-DWT redundancy. By reducing this redundancy, quite significant improvement can be obtained as shown in this paper. In the real image world, edges which are discontinuities are very important in presenting an image. DWT on edges is not as efficient as it is on smooth areas, so some correlated DWT residuals around an edge can be observed in our experiments. This is the most important reason why the post-DWT redundancy exists. To make use of this redundancy, two useful techniques are employed in this paper. They are the Magnitude Partition and the Coordinate Splitting. The first one does not increase data entropy while the second one could reduce data entropy. The combination of this two techniques is the key idea to the schemes of this paper. Since this post-DWT redundancy has not been well pointed out in the current literacy, the novelty of this paper is to give an overall examination on it and to provide the useful schemes to reduce it.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yi Wang and Ezzatollah Salari "Post-wavelet-transform redundancy and its reduction techniques for image compression", Proc. SPIE 2418, Still-Image Compression, (3 March 1995); https://doi.org/10.1117/12.204126
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Cited by 2 scholarly publications.
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KEYWORDS
Discrete wavelet transforms

Image compression

Wavelet transforms

Wavelets

Linear filtering

Quantization

Image filtering

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