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This article is part of the series Patches in Vision.

Open Access Research Article

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

DamonM Chandler1, MatthewD Gaubatz2* and SheilaS Hemami3

Author Affiliations

1 School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA

2 Print Production Automation Lab, HP Labs, Hewlett-Packard, Palo Alto, CA 94304, USA

3 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA

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EURASIP Journal on Image and Video Processing 2009, 2009:649316  doi:10.1155/2009/649316

The electronic version of this article is the complete one and can be found online at: http://jivp.eurasipjournals.com/content/2009/1/649316


Received:26 May 2008
Accepted:25 December 2008
Published:13 April 2009

© 2009 The Author(s).

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.

Abstract

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.

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