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  • Research Article
  • Open Access

A Patch-Based Structural Masking Model with an Application to Compression

  • 1,
  • 2Email author and
  • 3
EURASIP Journal on Image and Video Processing20092009:649316

  • Received: 26 May 2008
  • Accepted: 25 December 2008
  • Published:


The ability of an image region to hide or mask a given target signal continues to play a key role in the design of numerous image processing and vision systems. However, current state-of-the-art models of visual masking have been optimized for artificial targets placed upon unnatural backgrounds. In this paper, we (1) measure the ability of natural-image patches in masking distortion; (2) analyze the performance of a widely accepted standard masking model in predicting these data; and (3) report optimal model parameters for different patch types (textures, structures, and edges). Our results reveal that the standard model of masking does not generalize across image type; rather, a proper model should be coupled with a classification scheme which can adapt the model parameters based on the type of content contained in local image patches. The utility of this adaptive approach is demonstrated via a spatially adaptive compression algorithm which employs patch-based classification. Despite the addition of extra side information and the high degree of spatial adaptivity, this approach yields an efficient wavelet compression strategy that can be combined with very accurate rate-control procedures.


  • Image Patch
  • Side Information
  • Compression Algorithm
  • Patch Type
  • Compression Strategy

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Authors’ Affiliations

School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Print Production Automation Lab, HP Labs, Hewlett-Packard, Palo Alto, CA 94304, USA
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA


© The Author(s). 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.