No-Reference quality assessment of noisy images with local features and visual saliency models

Image quality assessment (IQA) measures predict the perceived quality of evaluated images, aiming to replace time-consuming human evaluation. This is particularly important for the automatic comparison of image processing techniques which often modify image content. Since the presence of noise highly affects the perception of images and only a few IQA measures address this type of distortion, a new no-reference IQA measure is proposed in this paper. In the introduced measure, two aspects of the Human Visual System (HVS) are considered. The sensitivity of the HVS to local image distortions is expressed by similarities between descriptors obtained with Speeded-Up Robust Features (SURF) technique. In order to improve the quality prediction, image patches for description are extracted from a gradient map of an input image. Noise also affects the visual saliency, since it decreases attention caused by image content. In the approach, visual saliency models of image patches are obtained and evaluated using adapted Visual Saliency-based Index (VSI). Experimental evaluation on popular IQA benchmarks reveals that the proposed measure outperforms the related state-of-the-art techniques. The applicability of the proposed method to an automatic selection of the best denoising algorithm is also discussed in the paper. (C) 2019 Elsevier Inc. All rights reserved.