An image multiresolution representation for lossless and lossy compression

• Published in: IEEE transactions on image processing Vol. 5; no. 9; pp. 1303 - 1310
• Main Authors: Said, A., Pearlman, W.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.1996; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Entropy; Exact sciences and technology; Filtering; Image coding; Image processing; Image resolution; Information, signal and communications theory; Inspection; Performance loss; Pixel; Predictive coding; Propagation losses; Rate distortion theory; Signal processing; Telecommunications and information theory; Signal compression; Image processing; Coding; Decomposition method; Mathematical method; Image transmission; Functional transformation
• ISSN: 1057-7149
• DOI: 10.1109/83.535842

We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation, the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate versus distortion performance is comparable to those of the most efficient lossy compression methods.