A reduced-reference metric based on the interest points in color images

In the last decade, an important research effort has been dedicated to quality assessment from subjective and objective points of view. The focus was mainly on Full Reference (FR) metrics because of the ability to compare to an original. Only few works were oriented to Reduced Reference (RR) or No Reference (NR) metrics, very useful for applications where the original image is not available such as transmission or monitoring. In this work, we propose a RR metric based on two concepts, the interest points of the image and the objects saliency on color images. This metric needs a very low amount of data (lower than 8 bytes) to be able to compute the quality scores. The results show a high correlation between the metric scores and the human judgement and a better quality range than well-known metrics like PSNR or SSIM. Finally, interest points have shown that they can predict the quality of compressed color images.