NDLI: A binary Markov model for the quantized images and its rate/distortion optimization

Content Provider	IEEE Xplore Digital Library
Author	Servetto, S. Rosenblatt, J.M. Ramchandran, K.
Copyright Year	1997
Description	Author affiliation: Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA (Servetto, S.)
Abstract	Zerotree based algorithms represent the state of the art in wavelet based image coding. At a high level, these algorithms can be described as first sending a map of the locations of the zero coefficients (the set of zerotree symbols), and then sending the value of nonzero coefficients. However, the decision of what map to send is typically made using some simplifying assumption on the structure of the map, motivated by some empirically observed property of the data (e.g., that zero coefficients are likely to appear in tree structured sets). In this article, the map of the locations of the zero coefficients is optimally estimated as a hidden binary Markov random field (MRF). Algorithms are presented for the estimation of the hidden field given the observed wavelet coefficients, for encoding the field, and for encoding the data given the field estimate. Simulation results show a very competitive rate/distortion performance of the coding algorithm, equal or superior to any published zerotree based image coder: this fact provides conclusive empirical evidence that the proposed model is appropriate for the data.
Starting Page	82
Ending Page	85
File Size	442839
Page Count	4
File Format	PDF
ISBN	0818681837
DOI	10.1109/ICIP.1997.631990
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet coefficients Rate distortion theory Image coding Context modeling Entropy Mathematics Data compression Engineering profession Constraint theory Algorithm design and analysis
Content Type	Text
Resource Type	Article

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