Special Section on Image Quality

Most apparent distortion: full-reference image quality assessment and the role of strategy

[+] Author Affiliations
Eric C. Larson

University of Washington, Department of Electrical Engineering, Seattle, Washington 98195

Damon M. Chandler

Oklahoma State University, School of Electrical and Computer Engineering, Image Coding and Analysis Lab, Stillwater, Oklahoma 74078

J. Electron. Imaging. 19(1), 011006 (January 07, 2010). doi:10.1117/1.3267105
History: Received May 01, 2009; Revised July 15, 2009; Accepted July 30, 2009; Published January 07, 2010
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The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure/information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image’s subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.

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Citation

Eric C. Larson and Damon M. Chandler
"Most apparent distortion: full-reference image quality assessment and the role of strategy", J. Electron. Imaging. 19(1), 011006 (January 07, 2010). ; http://dx.doi.org/10.1117/1.3267105


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