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

Eric Cooper Larson; Damon Michael Chandler

doi:10.1117/1.3267105

1 January 2010 Most apparent distortion: full-reference image quality assessment and the role of strategy

Eric Cooper Larson, Damon Michael Chandler

Author Affiliations +

Journal of Electronic Imaging, Vol. 19, Issue 1, 011006 (January 2010). https://doi.org/10.1117/1.3267105

Abstract

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.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Eric Cooper Larson and Damon Michael Chandler "Most apparent distortion: full-reference image quality assessment and the role of strategy," Journal of Electronic Imaging 19(1), 011006 (1 January 2010). https://doi.org/10.1117/1.3267105

Published: 1 January 2010

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