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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Published inarXiv.org
Main Authors Ignatov, Andrey, Timofte, Radu, Thang Van Vu, Luu, Tung Minh, Pham, Trung X, Cao Van Nguyen, Kim, Yongwoo, Jae-Seok Choi, Kim, Munchurl, Huang, Jie, Jiewen Ran, Chen, Xing, Zhou, Xingguang, Zhu, Pengfei, Geng, Mingrui, Li, Yawei, Agustsson, Eirikur, Gu, Shuhang, Luc Van Gool, de Stoutz, Etienne, Kobyshev, Nikolay, Nie, Kehui, Zhao, Yan, Li, Gen, Tong, Tong, Gao, Qinquan, Liu Hanwen, Pablo Navarrete Michelini, Zhu, Dan, Hu Fengshuo, Zheng, Hui, Wang, Xiumei, Deng, Lirui, Rang Meng, Qin, Jinghui, Shi, Yukai, Wen, Wushao, Lin, Liang, Feng, Ruicheng, Wu, Shixiang, Chao, Dong, Yu, Qiao, Subeesh Vasu, Madam, Nimisha Thekke, Kandula, Praveen, Rajagopalan, A N, Liu, Jie, Jung, Cheolkon
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.10.2018
Subjects
Image enhancement
Image resolution
Machine learning
Run time (computers)
Smartphones
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Summary: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.
ISSN:2331-8422