Centering of context-dependent components of prediction-error distributions of images

Glen G. Langdon Jr.; Mareboyana Manohar

doi:10.1117/12.158637

20 October 1993 Centering of context-dependent components of prediction-error distributions of images

Glen G. Langdon Jr., Mareboyana Manohar

Proceedings Volume 2028, Applications of Digital Image Processing XVI; (1993) https://doi.org/10.1117/12.158637
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

Traditionally the distribution of the prediction error has been treated as the single-parameter Laplacian distribution, and based on this assumption one can design a set of Huffman codes selected through an estimate of the parameter. More recently, the prediction error distribution has been compared to the Gaussian distribution about mean zero when the value is relatively high. However when using nearly quantized prediction errors in the context model, the relatively high variance case is seen to merge conditional distributions surrounding both positive edges and negative edges. Edge information is available respectively from large negative or positive prediction errors in the neighboring pixel positions. In these cases, the mean of the distribution is usually not zero. By separating these two cases, making appropriate assumptions on the mean of the context-dependent error distribution, and other techniques, additional cost-effective compression can be achieved.

Citation Download Citation

Glen G. Langdon Jr. and Mareboyana Manohar "Centering of context-dependent components of prediction-error distributions of images", Proc. SPIE 2028, Applications of Digital Image Processing XVI, (20 October 1993); https://doi.org/10.1117/12.158637

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