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Parameter reduction and context selection for compression of gray-scale images

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3 Author(s)
Todd, Stephen ; IBM United Kingdom Scientific Centre, Athelstan House, St. Clement Street, Winchester, Hunts SO23 9DR, England, UK ; Langdon, G.G., Jr. ; Rissanen, J.

In the compression of multilevel (color or gray) image data, effective compression is obtained economically by judicial selection of the predictor and the conditioning states or contexts which determine what probability distribution to use for the prediction error. We provide a cost-effective approach to the following two problems: (1) to reduce the number of coding parameters to describe a distribution when several contexts are involved, and (2) to choose contexts for which variations in prediction error distributions are expected. We solve Problem 1 (distribution description) by a partition of the range of values of the outcomes into equivalence classes, called buckets. The result is a special decomposition of the error range. Cost-effectiveness is achieved by using the many contexts only to predict the bucket (equivalence class) probabilities. The probabilities of the value within the bucket are assumed to be independent of the context, thus enormously reducing the number of coding parameters involved. We solve Problem 2 (economical contexts) by using the buckets of the surrounding pixels as components of the conditioning class. The bucket values have the desirable properties needed for the error distributions.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:29 ,  Issue: 2 )