Adaptive prediction trees for image compression

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)2131-2145
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number8
Publication statusPublished - Aug 2006


  • data compression
  • predictive coding
  • still-image coding

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