Comparison of Image Quality Assessment Algorithms on Compressed Images

A crucial step in image compression is the evaluation of its performance, and more precisely the available way to measure the final quality of the compressed image. Usually, to measure performance, some measure of the covariation between the subjective ratings and the degree of compression is performed between rated image quality and algorithm. Nevertheless, local variations are not well taken into account. We use the recently introduced Maximum Likelihood Difference Scaling (MLDS) method to quantify suprathreshold perceptual differences between pairs of images and examine how perceived image quality estimated through MLDS changes the compression rate is increased. This approach circumvents the limitations inherent to subjective rating methods.