Blind tone mapped image quality assessment with image segmentation and visual perception

Abstract With tone mapping, high dynamic range (HDR) image contents can be displayed on low dynamic range (LDR) display devices, in which some important visual information may be distorted. Thus, the tone mapped image (TMI) quality assessment is one of important issues in HDR image/video processing fields. Considering the difference of visual distortion degrees between the flat and complex regions in TMI, and considering that high-quality TMI should preserve as much information as possible of its original HDR image especially in the high/low luminance regions, this paper proposes a new blind TMI quality assessment method with image segmentation and visual perception. First, we design different features to describe the distortion of TMI’s different regions with two kinds of TMI segmentation. Then, considering that there lacks an efficient algorithm to quantify the importance of features, a feature clustering scheme is designed to eliminate the poor effect feature components in the extracted features to improve the effectiveness of the selected features. Finally, considering the diversity of tone mapping operator (TMO), which may cause global and local distortion of TMI, some other global features are also combined. At last, a final feature vector is formed to synthetically describe the distortion in TMI and used to blindly predict the TMI’s quality. Experimental results in the public ESPL-LIVE HDR database show that the Pearson linear correlation coefficient and Spearman rank order correlation coefficient of the proposed method reach 0.8302 and 0.7887, respectively, which is superior to the state-of-the-art blind TMI quality assessment methods, and it means that the proposed method is highly consistent with human visual perception.