PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling...
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| creator | Ignatov, Andrey Timofte, Radu Van Vu, Thang Luu, Tung Minh Pham, Trung X Van Nguyen, Cao Kim, Yongwoo Choi, Jae-Seok Kim, Munchurl Huang, Jie Ran, Jiewen Xing, Chen Zhou, Xingguang Zhu, Pengfei Geng, Mingrui Li, Yawei Agustsson, Eirikur Gu, Shuhang Van Gool, Luc de Stoutz, Etienne Kobyshev, Nikolay Nie, Kehui Zhao, Yan Li, Gen Tong, Tong Gao, Qinquan Hanwen, Liu Michelini, Pablo Navarrete Dan, Zhu Fengshuo, Hu Hui, Zheng Wang, Xiumei Deng, Lirui Meng, Rang Qin, Jinghui Shi, Yukai Wen, Wushao Lin, Liang Feng, Ruicheng Wu, Shixiang Dong, Chao Qiao, Yu Vasu, Subeesh Madam, Nimisha Thekke Kandula, Praveen Rajagopalan, A. N Liu, Jie Jung, Cheolkon |
| description | This paper reviews the first challenge on efficient perceptual image
enhancement with the focus on deploying deep learning models on smartphones.
The challenge consisted of two tracks. In the first one, participants were
solving the classical image super-resolution problem with a bicubic downscaling
factor of 4. The second track was aimed at real-world photo enhancement, and
the goal was to map low-quality photos from the iPhone 3GS device to the same
photos captured with a DSLR camera. The target metric used in this challenge
combined the runtime, PSNR scores and solutions' perceptual results measured in
the user study. To ensure the efficiency of the submitted models, we
additionally measured their runtime and memory requirements on Android
smartphones. The proposed solutions significantly improved baseline results
defining the state-of-the-art for image enhancement on smartphones. |
| doi_str_mv | 10.48550/arxiv.1810.01641 |
| format | Article |
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enhancement with the focus on deploying deep learning models on smartphones.
The challenge consisted of two tracks. In the first one, participants were
solving the classical image super-resolution problem with a bicubic downscaling
factor of 4. The second track was aimed at real-world photo enhancement, and
the goal was to map low-quality photos from the iPhone 3GS device to the same
photos captured with a DSLR camera. The target metric used in this challenge
combined the runtime, PSNR scores and solutions' perceptual results measured in
the user study. To ensure the efficiency of the submitted models, we
additionally measured their runtime and memory requirements on Android
smartphones. The proposed solutions significantly improved baseline results
defining the state-of-the-art for image enhancement on smartphones.</description><identifier>DOI: 10.48550/arxiv.1810.01641</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2018-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1810.01641$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1810.01641$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ignatov, Andrey</creatorcontrib><creatorcontrib>Timofte, Radu</creatorcontrib><creatorcontrib>Van Vu, Thang</creatorcontrib><creatorcontrib>Luu, Tung Minh</creatorcontrib><creatorcontrib>Pham, Trung X</creatorcontrib><creatorcontrib>Van Nguyen, Cao</creatorcontrib><creatorcontrib>Kim, Yongwoo</creatorcontrib><creatorcontrib>Choi, Jae-Seok</creatorcontrib><creatorcontrib>Kim, Munchurl</creatorcontrib><creatorcontrib>Huang, Jie</creatorcontrib><creatorcontrib>Ran, Jiewen</creatorcontrib><creatorcontrib>Xing, Chen</creatorcontrib><creatorcontrib>Zhou, Xingguang</creatorcontrib><creatorcontrib>Zhu, Pengfei</creatorcontrib><creatorcontrib>Geng, Mingrui</creatorcontrib><creatorcontrib>Li, Yawei</creatorcontrib><creatorcontrib>Agustsson, Eirikur</creatorcontrib><creatorcontrib>Gu, Shuhang</creatorcontrib><creatorcontrib>Van Gool, Luc</creatorcontrib><creatorcontrib>de Stoutz, Etienne</creatorcontrib><creatorcontrib>Kobyshev, Nikolay</creatorcontrib><creatorcontrib>Nie, Kehui</creatorcontrib><creatorcontrib>Zhao, Yan</creatorcontrib><creatorcontrib>Li, Gen</creatorcontrib><creatorcontrib>Tong, Tong</creatorcontrib><creatorcontrib>Gao, Qinquan</creatorcontrib><creatorcontrib>Hanwen, Liu</creatorcontrib><creatorcontrib>Michelini, Pablo Navarrete</creatorcontrib><creatorcontrib>Dan, Zhu</creatorcontrib><creatorcontrib>Fengshuo, Hu</creatorcontrib><creatorcontrib>Hui, Zheng</creatorcontrib><creatorcontrib>Wang, Xiumei</creatorcontrib><creatorcontrib>Deng, Lirui</creatorcontrib><creatorcontrib>Meng, Rang</creatorcontrib><creatorcontrib>Qin, Jinghui</creatorcontrib><creatorcontrib>Shi, Yukai</creatorcontrib><creatorcontrib>Wen, Wushao</creatorcontrib><creatorcontrib>Lin, Liang</creatorcontrib><creatorcontrib>Feng, Ruicheng</creatorcontrib><creatorcontrib>Wu, Shixiang</creatorcontrib><creatorcontrib>Dong, Chao</creatorcontrib><creatorcontrib>Qiao, Yu</creatorcontrib><creatorcontrib>Vasu, Subeesh</creatorcontrib><creatorcontrib>Madam, Nimisha Thekke</creatorcontrib><creatorcontrib>Kandula, Praveen</creatorcontrib><creatorcontrib>Rajagopalan, A. N</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><creatorcontrib>Jung, Cheolkon</creatorcontrib><title>PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report</title><description>This paper reviews the first challenge on efficient perceptual image
enhancement with the focus on deploying deep learning models on smartphones.
The challenge consisted of two tracks. In the first one, participants were
solving the classical image super-resolution problem with a bicubic downscaling
factor of 4. The second track was aimed at real-world photo enhancement, and
the goal was to map low-quality photos from the iPhone 3GS device to the same
photos captured with a DSLR camera. The target metric used in this challenge
combined the runtime, PSNR scores and solutions' perceptual results measured in
the user study. To ensure the efficiency of the submitted models, we
additionally measured their runtime and memory requirements on Android
smartphones. The proposed solutions significantly improved baseline results
defining the state-of-the-art for image enhancement on smartphones.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj81Kw0AUhWfjQlofwJXzAqlzO39JdxKiBlostftwk7kxhWQSplH07Z1WVwe-A4fzMXYPYqVSrcUjhu_T1wrSCAQYBbes2JeHHc877HvyH8RHz_cUGprmT-x5OWBkhe_QNzSQny_9-4BhnrrR03nDDzSNYV6ymxb7M93954Idn4tj_pps317K_GmboLGQgHWN0BJTBa0StdMZKdNqS-tIRaycAGfSdYbWOVtnsgWpZWMkAtRSSLlgD3-zV49qCqd45ae6-FRXH_kL51ND-w</recordid><startdate>20181003</startdate><enddate>20181003</enddate><creator>Ignatov, Andrey</creator><creator>Timofte, Radu</creator><creator>Van Vu, Thang</creator><creator>Luu, Tung Minh</creator><creator>Pham, Trung X</creator><creator>Van Nguyen, Cao</creator><creator>Kim, Yongwoo</creator><creator>Choi, Jae-Seok</creator><creator>Kim, Munchurl</creator><creator>Huang, Jie</creator><creator>Ran, Jiewen</creator><creator>Xing, Chen</creator><creator>Zhou, Xingguang</creator><creator>Zhu, Pengfei</creator><creator>Geng, Mingrui</creator><creator>Li, Yawei</creator><creator>Agustsson, Eirikur</creator><creator>Gu, Shuhang</creator><creator>Van Gool, Luc</creator><creator>de Stoutz, Etienne</creator><creator>Kobyshev, Nikolay</creator><creator>Nie, Kehui</creator><creator>Zhao, Yan</creator><creator>Li, Gen</creator><creator>Tong, Tong</creator><creator>Gao, Qinquan</creator><creator>Hanwen, Liu</creator><creator>Michelini, Pablo Navarrete</creator><creator>Dan, Zhu</creator><creator>Fengshuo, Hu</creator><creator>Hui, Zheng</creator><creator>Wang, Xiumei</creator><creator>Deng, Lirui</creator><creator>Meng, Rang</creator><creator>Qin, Jinghui</creator><creator>Shi, Yukai</creator><creator>Wen, Wushao</creator><creator>Lin, Liang</creator><creator>Feng, Ruicheng</creator><creator>Wu, Shixiang</creator><creator>Dong, Chao</creator><creator>Qiao, Yu</creator><creator>Vasu, Subeesh</creator><creator>Madam, Nimisha Thekke</creator><creator>Kandula, Praveen</creator><creator>Rajagopalan, A. 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N</au><au>Liu, Jie</au><au>Jung, Cheolkon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report</atitle><date>2018-10-03</date><risdate>2018</risdate><abstract>This paper reviews the first challenge on efficient perceptual image
enhancement with the focus on deploying deep learning models on smartphones.
The challenge consisted of two tracks. In the first one, participants were
solving the classical image super-resolution problem with a bicubic downscaling
factor of 4. The second track was aimed at real-world photo enhancement, and
the goal was to map low-quality photos from the iPhone 3GS device to the same
photos captured with a DSLR camera. The target metric used in this challenge
combined the runtime, PSNR scores and solutions' perceptual results measured in
the user study. To ensure the efficiency of the submitted models, we
additionally measured their runtime and memory requirements on Android
smartphones. The proposed solutions significantly improved baseline results
defining the state-of-the-art for image enhancement on smartphones.</abstract><doi>10.48550/arxiv.1810.01641</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report |
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