F-MAD: a feature-based extension of the most apparent distortion algorithm for image quality assessment

Punit Singh; Damon M. Chandler

doi:10.1117/12.2005153

4 February 2013 F-MAD: a feature-based extension of the most apparent distortion algorithm for image quality assessment

Punit Singh, Damon M. Chandler

Proceedings Volume 8653, Image Quality and System Performance X; 86530I (2013) https://doi.org/10.1117/12.2005153
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

In this paper, we describe the results of a study designed to investigate the effectiveness of peak signal-to-noise ratio (PSNR) as a quality estimator when measured in various feature domains. Although PSNR is well known to be a poor predictor of image quality, PSNR has been shown be quite effective for additive, pixel-based distortions. We hypothesized that PSNR might also be effective for other types of distortions which induce changes to other visual features, as long as PSNR is measured between local measures of such features. Given a reference and distorted image, five feature maps are measured for each image (lightness distance, color distance, contrast, edge strength, and sharpness). We describe a variant of PSNR in which quality is estimated based on the extent to which these feature maps for the reference image differ from the corresponding maps for the distorted image. We demonstrate how this feature-based approach can lead to improved estimators of image quality.

Citation Download Citation

Punit Singh and Damon M. Chandler "F-MAD: a feature-based extension of the most apparent distortion algorithm for image quality assessment", Proc. SPIE 8653, Image Quality and System Performance X, 86530I (4 February 2013); https://doi.org/10.1117/12.2005153

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