No reference image quality assessment metric via multi-domain structural information and piecewise regression.

The general purpose no reference image quality assessment (NR-IQA) is a challenging task, which faces two hurdles: (1) it is difficult to develop one quality aware feature which works well across different types of distortion and (2) it is hard to learn a regression model to approximate a complex distribution for all training samples in the feature space. In this paper, we propose a novel NR-IQA method that addresses these problems by introducing the multi-domain structural information and piecewise regression. The main motivation of our method is based on two points. Firstly, we develop a new local image representation which extracts the structural image information from both the spatial-frequency and spatial domains. This multi-domain description could better capture human vision property. By combining our local features with a complementary global feature, the discriminative power of each single feature could be further improved. Secondly, we develop an efficient piecewise regression method to capture the local distribution of the feature space. Instead of minimizing the fitting error for all training samples, we train the specific prediction model for each query image by adaptive online learning, which focuses on approximating the distribution of the current test image’s k -nearest neighbor (KNN). Experimental results on three benchmark IQA databases (i.e., LIVE II, TID2008 and CSIQ) show that the proposed method outperforms many representative NR-IQA algorithms. [ABSTRACT FROM AUTHOR]