An efficient approach for no-reference image quality assessment based on statistical texture and structural features

Abstract

This paper presents a rotation-invariant and computationally efficient no-reference image quality assessment (NR-IQA) model. It estimates the image quality based on texture and structural information associated with the images. The human visual system (HVS) uses perceptual features such as texture and structure as primary information to understand the visual scene and the image content. Moreover, the texture and structural information capture the loss of naturalness due to distortions in the image. Therefore, in this work, the important texture features are extracted using the local binary patterns (LBP).

The modified LBP, also known as the hyper-smoothing LBP (H-LBP) and Laplacian of H-LBP (LH-LBP), represents the image structure. Further, the image quality prediction model computes the quality of the image based on the statistical feature measures of the texture and structural information. In the proposed approach, the image quality prediction model uses support vector regression (SVR) to measure image quality. Various experimentations are carried out on the LIVE and TID2013 database to test the effectiveness of the proposed NR-IQA model. The performance metrics such as Spearman rank-ordered correlation coefficient, Pearson linear correlation coefficient, and root mean square error is computed to show the efficiency of the presented approach. The experimental results illustrate a high correlation between the predicted quality score and the human visual perceptions. It is also found to be competitive with the best-performing full-reference and no-reference IQA models.