色调映射
计算机科学
高动态范围
人工智能
特征(语言学)
水准点(测量)
可视化
自然性
质量(理念)
模式识别(心理学)
图像质量
航程(航空)
计算机视觉
特征提取
动态范围
图像(数学)
工程类
地理
物理
航空航天工程
哲学
认识论
量子力学
语言学
大地测量学
作者
Qiuping Jiang,Feng Shao,Lin Wang,Gangyi Jiang
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2019-02-01
卷期号:29 (2): 323-335
被引量:48
标识
DOI:10.1109/tcsvt.2017.2783938
摘要
High dynamic range (HDR) image, which has a powerful capacity to represent the wide dynamic range of real-world scenes, has been receiving attention from both academic and industrial communities. Although HDR imaging devices have become prevalent, the display devices for HDR images are still limited. To facilitate the visualization of HDR images in standard low dynamic range displays, many different tone mapping operators (TMOs) have been developed. To create a fair comparison of different TMOs, this paper proposes a BLInd QUality Evaluator to blindly predict the quality of Tone-Mapped Images (BLIQUE-TMI) without accessing the corresponding HDR versions. BLIQUE-TMI measures the quality of TMIs by considering the following aspects: 1) visual information; 2) local structure; and 3) naturalness. To be specific, quality-aware features related to the former two aspects are extracted in a local manner based on sparse representation, while quality-aware features related to the third aspect are derived based on global statistics modeling in both intensity and color domains. All the extracted local and global quality-aware features constitute a final feature vector. An emergent machine learning technique, i.e., extreme learning machine, is adopted to learn a quality predictor from feature space to quality space. The superiority of BLIQUE-TMI to several leading blind IQA metrics is well demonstrated on two benchmark databases.
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