Blind Quality Metric for Multidistortion Images Based on Cartoon and Texture Decomposition
Feiyan Zhang, Badri Roysam · IEEE Signal Processing Letters · 2016
In this letter, a no-reference (NR) hybrid image quality assessment (IQA) metric based on cartoon-texture decomposition (CTD) is presented. Focusing on images distorted by both blur and noise, the method takes properties of CTD to separate image into cartoon part with salient edges and texture part with noises. Then, the blur degree and noise level can be estimated separately from different image parts, combine with a joint effect prediction between blur and noise distortions, we present a cartoon-texture decomposition-based blind metric (CTDBBM). Comparative studies with classical full-reference IQA metrics and state-of-the-art NR metrics are conducted on multidistortion image database: LIVEMD. Experimental results show that the CTDBBM performs well and has a high consistency with the human opinions given in the database.