BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/142228 |
| _version_ | 1824454254439432192 |
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| author | Jiang, Qiuping Shao, Feng Lin, Weisi Jiang, Gangyi |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Jiang, Qiuping Shao, Feng Lin, Weisi Jiang, Gangyi |
| author_sort | Jiang, Qiuping |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:19:23Z |
| format | Journal Article |
| id | ntu-10356/142228 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:19:23Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1422282020-06-17T08:09:09Z BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses Jiang, Qiuping Shao, Feng Lin, Weisi Jiang, Gangyi School of Computer Science and Engineering Engineering::Computer science and engineering Tone Mapping Image Quality Assessment 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. 2020-06-17T08:09:09Z 2020-06-17T08:09:09Z 2017 Journal Article 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 1051-8215 https://hdl.handle.net/10356/142228 10.1109/TCSVT.2017.2783938 2-s2.0-85038876389 2 29 323 335 en IEEE Transactions on Circuits and Systems for Video Technology © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Tone Mapping Image Quality Assessment Jiang, Qiuping Shao, Feng Lin, Weisi Jiang, Gangyi BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses |
| title | BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses |
| title_full | BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses |
| title_fullStr | BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses |
| title_full_unstemmed | BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses |
| title_short | BLIQUE-TMI : blind quality evaluator for tone-mapped images based on local and global feature analyses |
| title_sort | blique tmi blind quality evaluator for tone mapped images based on local and global feature analyses |
| topic | Engineering::Computer science and engineering Tone Mapping Image Quality Assessment |
| url | https://hdl.handle.net/10356/142228 |
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