We previously proposed a lossless imagecoding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate t...
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We previously proposed a lossless imagecoding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.
This paper presents a complete general-purpose method for still-image compression called adaptive prediction trees. Efficient lossy and lossless compression of photographs, graphics, textual, and mixed images is achie...
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This paper presents a complete general-purpose method for still-image compression called adaptive prediction trees. Efficient lossy and lossless compression of photographs, graphics, textual, and mixed images is achieved by ordering the data in a multicomponent binary pyramid, applying an empirically optimized nonlinear predictor, exploiting structural redundancies between color components, then coding with hex-trees and adaptive runlength/Huffman coders. Color palettization and order statistics prefiltering are applied adaptively as appropriate. Over a diverse image test set, the method outperforms standard lossless and lossy alternatives. The competing lossy alternatives use block transforms and wavelets in well-studied configurations. A major result of this paper is that predictive coding is a viable and sometimes preferable alternative to these methods.
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