No-Reference Image Quality Assessment Using Image Statistics and Robust Feature Descriptors

Abstract

The aim of no-reference image quality assessment (NR-IQA) techniques is to measure the perceptual quality of an image without access to the reference image. In this letter, a novel NR-IQA measure is introduced in which quality-aware statistics are used as perceptual features for the quality prediction. In the method, the distorted image is converted to grayscale and filtered using gradient operators. Then, the speeded-up robust feature (SURF) technique is employed to detect and describe keypoints in obtained images. The SURF interest point detection method is affected by distortions in the filtered image. Therefore, it can be used to reflect the decreased attention of the human visual system caused by image distortions. In the method, statistics are calculated for processed images and their SURF descriptors. Finally, they are mapped into subjective opinion scores using a support vector regression technique. The experimental evaluation conducted on four demanding large benchmark datasets, which contain images corrupted by single and multiple distortions, demonstrates that the proposed technique outperforms the state-of-the-art NR measures.