Adaptive prediction trees for image compression

J.A. Robinson

doi:10.1109/TIP.2006.875196

Adaptive prediction trees for image compression

J.A. Robinson

Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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

Original language	English
Pages (from-to)	2131-2145
Number of pages	14
Journal	IEEE Transactions on Image Processing
Volume	15
Issue number	8
DOIs	https://doi.org/10.1109/TIP.2006.875196
Publication status	Published - Aug 2006

Keywords

data compression
predictive coding
still-image coding
LOSSLESS

Access to Document

10.1109/TIP.2006.875196

http://dx.doi.org/10.1109/TIP.2006.875196

Cite this

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title = "Adaptive prediction trees for image compression",

abstract = "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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AB - 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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