Share Email Print
cover

Proceedings Paper

Experiments in lossless and virtually lossless image compression algorithms
Author(s): Glen G. Langdon; Chris A. Haidinyak
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The CB9 lossless image compression algorithm is context-based, and codes prediction errors with an adaptive arithmetic code. It has been developed within an algorithm class that includes (in the order of their development) Sunset, JPEG lossless, sub8xb, and now CaTH (Centering and Tail Handling). Lossless compression algorithms using prediction errors are easily modified to introduce a small loss through quantization so that the absolute error for any pixel location does not exceed prescribed value N. In this case, N varies from 1 to 7; the values for which the JPEG group issued a call for contributions. This work describes CB9 and the experiments with near-lossless compression using the JPEG test images. Included are experiments with some image processing operations such as edge-enhancement with the purpose of studying the loss in fidelity from successively performing decompression, followed by an image processing operation, followed by recompression of the new result.

Paper Details

Date Published: 3 March 1995
PDF: 7 pages
Proc. SPIE 2418, Still-Image Compression, (3 March 1995); doi: 10.1117/12.204135
Show Author Affiliations
Glen G. Langdon, Univ. of California/Santa Cruz (United States)
Chris A. Haidinyak, Univ. of California/Santa Cruz and Silicon Systems (United States)


Published in SPIE Proceedings Vol. 2418:
Still-Image Compression
Majid Rabbani; Edward J. Delp; Sarah A. Rajala, Editor(s)

© SPIE. Terms of Use
Back to Top