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|Title:||BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses||Authors:||Jiang, Qiuping
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2017||Source:||Jiang, Q., Shao, F., Lin, W., & Jiang, G. (2019). BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses. IEEE Transactions on Circuits and Systems for Video Technology, 29(2), 323-335. doi:10.1109/TCSVT.2017.2783938||Journal:||IEEE Transactions on Circuits and Systems for Video Technology||Abstract:||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.||URI:||https://hdl.handle.net/10356/142228||ISSN:||1051-8215||DOI:||10.1109/TCSVT.2017.2783938||Rights:||© 2017 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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