Reduced-Reference image quality assessment based on 2-D discrete FFT and Edge Similarity

Abstract

Reduced-Reference (RR) image quality measures aim to predict the perceptual quality of distorted image using only partial information about the original image. In this paper, an effective Reduced-Reference image quality assessment algorithm based on FFT transform and Edge Similarity is introduced. The main design principle of the proposed method is choice of the best blocks of Image. After dividing the source images into blocks of 16×16 pixels, calculating the FFT Transform for each block, the FFT Transform gives best blocks of image. Next, the important features blocks of the image were recognized by Edge and the same actions were done on the image of distortions and finally, the similarity of both images was calculated. The experimental results on LIVE and CSIQ databases show that our RR proposed metric correlates well with the subjective quality scores, also in comparison with commonly used full-reference metric and with a state-of-the-art reduced reference.