Efficient feature selection for Blind Image Quality Assessment based on natural scene statistics

Abstract

Blind Image Quality Assessment (BIQA) has received considerable importance with the increase in the use of multimedia in our daily lives. The main objective of BIQA is to predict the quality of distorted images without any prior information about the original image. In this work, we propose an efficient feature selection method for blind image quality assessment based on natural scene statistics i.e., Distortion Identification-based Image Verity and Integrity Evaluation (DIIVINE). The proposed method produces better results for non-reference image quality assessment by selecting features, which produce the best Spearman Rank Order Correlation Constant (SROCC) scores averaged over 1000 random runs. The experimental results conducted on the LIVE database show that the proposed method strongly correlates to the subjective mean observer score and is competitive to the state-of-the-art image quality assessment techniques with a minimum number of features that reduces the computational expense.