Natural image utility assessment using image contours

In the quality assessment task, observers evaluate a natural image based on its perceptual resemblance to a reference. For the utility assessment task, observers evaluate the usefulness of a natural image as a surrogate for a reference. Humans generally use the information captured by an imaging system and tolerate distortions as long as the underlying task is performed reliably. Conventional notions of perceived quality cannot generally predict the perceived utility of a natural image. This paper examines variations to basic components of a recently introduced utility assessment algorithm that compares the contours of a reference and test image, referred to as the natural image contour evaluation (NICE), in terms of their capability to improve the prediction of perceived utility scores. Results show that classical edge-detection algorithms incorporated into NICE provide statistically equivalent performance to other, more complex edge-detection algorithms.