No-Reference Image Contrast Evaluation by Generating Bidirectional Pseudoreferences
Qiuping Jiang, Zhenyu Peng, Guanghui Yue, Hong Li, Feng Shao
Abstract
This article proposes a simple yet reliable no-reference image contrast evaluator (NICE) by generating bidirectional pseudoreferences (BPR). Different from the existing no-reference metrics that only operate on the contrast distorted image (CDI) itself, our proposed NICE-BPR measures the deviations of a CDI to its corresponding aggravated and enhanced counterparts (i.e., BPRs) in a hybrid feature space. Given a CDI, we first perform contrast aggravation and contrast enhancement using gamma correction and histogram equalization, respectively. Then, hybrid contrast-aware features are, respectively, extracted from the CDI and its corresponding BPRs via the analysis of histogram, entropy, and structure. The features obtained from the CDI are one-by-one compared with those from the BPRs to derive the bidirectional feature deviation vector. Finally, a quality predictor is built by learning a regression model to fuse the feature vector into a continuous quality score. Extensive experiments on several databases well-demonstrate the superiority of NICE-BPR.