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.