Reduced-reference image quality assessment based on internal generative mechanism utilizing shearlets and Rényi entropy analysis

During acquisition, processing, compression and transmission, images may be corrupted by multiple distortions such as blur, noise or compression artefacts. However, current image quality assessment (IQA) methods are often designed for images degraded by a single distortion type. This paper proposes a reduced-reference (RR) IQA method to predict the quality of multi-distorted images. The method is based on feature extraction from the reference and the distorted images. Based on internal generative mechanism (IGM) theory, the images are decomposed first into their predicted and disorderly portions. Next, several features are captured from each portion and feature differences are computed between the reference and distorted images. Finally, support vector regression (SVR) is adopted to obtain a quality score. The results on public multiply-distorted image databases, namely MDID2015 and MLIVE, show that the proposed method delivers higher accuracy than several image quality metrics.