NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Yang, Ren; Timofte, Radu; Zheng, Meisong; Xing, Qunliang; Qiao, Minglang; Xu, Mai; Jiang, Lai; Liu, Huaida; Chen, Ying; Ben, Youcheng; Zhou, Xiao; Fu, Yong Qing; Cheng, Pei; Yu, Gang; Li, Junyi; Wu, Ren‐Long; Zhang, Zhilu; Shang, Wei; Lv, Zhengyao; Chen, Yunjin; Zhou, Mingcai; Ren, Dongwei; Zhang, Kai; Zuo, Wangmeng; Ostyakov, Pavel; Vyal, Dmitry; Soltanayev, Shakarim; Chervontsev, Sergey; Zhussip, Magauiya; Zou, Xueyi; Michelini, Youliang Yan Pablo Navarrete; Lu, Yunhua; Zhang, Diankai; Liu, Shaoli; Gao, Si; Wu, Biao; Zheng, Chengjian; Zhang, Xiaofeng; Lu, Kaidi; Wang, Ning; Canh, Thuong Nguyen; Bach, Thong; Wang, Qing; Sun, Xingming; Ma, Haoyu; Zhao, Shijie; Li, Junlin; Xie, Liangbin; Shi, Shuwei; Yang, Yujiu; Wang, Xintao; Gu, Jinjin; Dong, Chao; Shi, Xiaodi; Nian, Chunmei; Jiang, Dong; Lin, Jie; Xie, Zhihuai; Ye, Mao; Luo, Dengyan; Peng, Liuhan; Chen, Shengjie; Wang, Qian; Liu, Xin; Liang, Boyang; Dong, Hang; Huang, Yuhao; Chen, Kai; Guo, Xingbei; Sun, Yujing; Huilei, Wu,; Wei, Pengxu; Huang, Yao-Wen; Chen, Junying; Lee, Ik Hyun; Khowaja, Sunder Ali; Yoon, Jiseok

doi:10.1109/cvprw56347.2022.00129

Cited by 27 publications

(8 citation statements)

References 99 publications

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“…This challenge is one of the NTIRE 2022 associated challenges: spectral recovery [7], spectral demosaicing [6], perceptual image quality assessment [19], inpainting [40], night photography rendering [17], efficient super-resolution [31], learning the super-resolution space [34], super-resolution and quality enhancement of compressed video [49], high dynamic range [38], stereo super-resolution [45], burst super-resolution [8].…”

Section: Introductionmentioning

confidence: 99%

NTIRE 2022 Spectral Recovery Challenge and Data Set

Arad¹,

Timofte²,

Yahel³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD 1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEGcompressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current stateof-the-art in spectral reconstruction from natural RGB images.

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Section: Introductionmentioning

confidence: 99%

NTIRE 2022 Spectral Recovery Challenge and Data Set

Arad¹,

Timofte²,

Yahel³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Self Cite

View full text Add to dashboard Cite

show abstract

“…This challenge is one of the NTIRE 2022 associated challenges: spectral recovery [6], spectral demosaicing [5], perceptual image quality assessment [26], inpainting [46], efficient super-resolution [35], learning the super-resolution space [41], super-resolution and quality enhancement of compressed video [55], high dynamic range [44], stereo super-resolution [52], burst super-resolution [8].…”

Section: Introductionmentioning

confidence: 99%

NTIRE 2022 Challenge on Night Photography Rendering

Ershov¹,

Savchik²,

Shepelev³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://nightimaging.org/

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“…This challenge is a part of the AIM 2022 Challenges: Real-Time Image Super-Resolution [12], Real-Time Video Super-Resolution [13], Single-Image Depth Estimation [15], Learned Smartphone ISP [16], Real-Time Rendering Realistic Bokeh [14], Compressed Input Super-Resolution [35] and Reversed ISP [10]. The results obtained in the other competitions and the description of the proposed solutions can be found in the corresponding challenge reports.…”

Section: Introductionmentioning

confidence: 99%

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Kınlı¹,

Menteş²,

Özcan³

et al. 2022

Preprint

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This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Cited by 27 publications

References 99 publications

NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Challenge on Night Photography Rendering

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

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