On the quality assessment of enhanced images: A database, analysis, and strategies for augmenting existing methods
Most methods of image quality assessment (QA) have been designed for QA of degraded images. This paper presents the results of a study designed to investigate whether existing QA methods can be adapted to succeed on enhanced images. We developed a database containing digitally enhanced images and associated subjective quality ratings. Next, we analyzed the efficacy of select QA methods and their reverse-mode versions in predicting the ratings. Given the fact that an enhanced image makes the original image appear degraded, we tested both normal and reverse-mode versions, where the latter were implemented by specifying the enhanced image as the reference and the original image as the “degraded” image. Our results demonstrate that this reverse-mode approach improves QA of enhanced images. We present a strategy for further improving the QA methods by using measures of contrast, sharpness, and color saturation.