Lossless and near-lossless image compression with successive refinement

İsmail Avcibaş, Nasir Memon, Bülent Sankur, Khalid Sayood

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We present a technique that provides progressive transmission and near-lossless compression in one single framework. The proposed technique produces a bitstream that results in progressive reconstruction of the image just like what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound after each layer of the successively refinable bitstream is decoded. We formulate the image data compression problem as one of asking the optimal questions to determine, respectively, the value or the interval of the pixel, depending on whether one is interested in lossless or near-lossless compression. New prediction methods based on the nature of the data at a given pass are presented and links to the existing methods are explored. The trade-off between non-causal prediction and data precision is discussed within the context of successive refinement. Context selection for prediction in different passes is addressed. Finally, experimental results for both lossless and near-lossless cases are presented, which are competitive with the state-of-the-art compression schemes.

Original languageEnglish (US)
Pages (from-to)41-52
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2001


  • Causal non-causal prediction
  • Density estimation
  • Embedded bit stream
  • Lossless compression
  • Near-lossless compression
  • Rate scalable compression
  • Successive refinement

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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