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Abstract

Image data to be compressed is first converted from the RGB domain into a gamma-powered YUV domain. A wavelet transform then separates image data into high- and low-detail sectors, incorporating a dynamic scaling method, allowing for optimal resolution. The output data from the wavelet transform is then quantized according to an entropy-prediction algorithm that tightly controls the final size of the processed image. An adaptive Golomb engine compresses the data using an adaptive form of Golomb encoding in which mean values are variable across the data. Using variable mean values reduces the deleterious effects found in conventional Golomb encoding in which localized regions of similar data are inefficiently coded if their bit values are uncommon in the data as a whole. Inverse functions are applied to uncompress the image, and a fractal dithering engine can additionally be applied to display an image on a display of lower color depth.

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First Claim