Reduced-reference quality assessment of multiply-distorted images based on structural and uncertainty information degradation

Abstract

The majority of existing objective Image Quality Assessment (IQA) methods are designed for evaluation of images corrupted by single distortion types. However, images may be degraded with multiple distortions during processing stages. In this paper, we propose a reduced-reference IQA algorithm to predict the quality of multiply-distorted images. An image is first decomposed into predicted and disorderly portions based on the internal generative mechanism theory. The structural information is captured from the predicted image by using a shearlet representation and Rényi directional entropy is deployed to measure the disorderly information changes. Finally, we introduce the application of a framework namely Learning Using Privileged Information (LUPI) to build a quality model and obtain quality scores. During training, the LUPI framework utilizes a set of additional privileged data to learn an improved quality model. Experimental results on multiply-distorted image datasets (MLIVE and MDID2015) confirm the effectiveness of the proposed IQA model.