NTIRE 2022 Challenge on Perceptual Image Quality Assessment

Gu, Jinjin; Cai, Haoming; Dong, Chao; Ren, Jimmy; Qiao, Yu; Gu, Shuhang; Timofte, Radu; Cheon, Manri; Yoon, Sungjun; Kang, Byungyeon; Lee, Junwoo; Zhang, Qing; Guo, Haiyang; Bin, Yi; Hou, Yi; Luo, Huiwu; Guo, Jingyu; Wang, Zirui; Hai, Wang; Yang, Wenming; Bai, Qingyan; Shi, Shuwei; Xia, Weihao; Cao, Mingdeng; Wang, Jiahao; Chen, Yifan; Yang, Yujiu; Li, Yang; Zhang, Tao; Feng, Longtao; Liao, Yi‐Chu; Li, Junlin; Thong, William; Pereira, J. C. F.; Leonardis, Aleš; McDonagh, Steven; Xu, Kele; Yang, Lehan; Cai, Hengxing; Sun, Pengfei; Ayyoubzadeh, Seyed Mehdi; Royat, Ali; Fezza, Sid Ahmed; Hammou, Dounia; Hamidouche, Wassim; Ahn, Sanghyun; Yoon, Gwangjin; Tsubota, Koki; Akutsu, Hiroaki; Aizawa, Kiyoharu

doi:10.1109/cvprw56347.2022.00109

Cited by 35 publications

(13 citation statements)

References 59 publications

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“…We proposed our GSN to participate in the NTIRE 2022 Perceptual Image Quality Assessment Challenge [1] Track 1 Full-Reference. The goal of this competition is to develop IQA methods with strong robustness and generalization capabilities that produce results showing high correla- in Tab.…”

Section: Ntire 2022 Perceptual Iqa Challengementioning

confidence: 99%

“…We experimented the proposed algorithm GSN on several publicly available datasets and proved its superior performance. Our network won the second place in NTIRE 2022 Perceptual Image Quality Assessment Challenge track 1 Full-Reference [1].…”

mentioning

confidence: 99%

“…The results of comparion experiment on NTIRE 2022[1] FR Track. The top two performing methods are highlighted in bold face.…”

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confidence: 99%

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Image Quality Assessment with Gradient Siamese Network

Cong¹,

Fu²,

Zhang³

et al. 2022

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In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment. The proposed method is skilled in capturing the gradient features between distorted images and reference images in full-reference image quality assessment (IQA) task. We utilize Central Differential Convolution to obtain both semantic features and detail difference hidden in image pair. Furthermore, spatial attention guides the network to concentrate on regions related to image detail. For the low-level, mid-level, and high-level features extracted by the network, we innovatively design a multi-level fusion method to improve the efficiency of feature utilization. In addition to the common mean square error supervision, we further consider the relative distance among batch samples and successfully apply KL divergence loss to the image quality assessment task. We experimented the proposed algorithm GSN on several publicly available datasets and proved its superior performance. Our network won the second place in NTIRE 2022 Perceptual Image Quality Assessment Challenge track 1 Full-Reference [1].

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Section: Ntire 2022 Perceptual Iqa Challengementioning

confidence: 99%

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confidence: 99%

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Image Quality Assessment with Gradient Siamese Network

Cong¹,

Fu²,

Zhang³

et al. 2022

Preprint

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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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“…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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NTIRE 2022 Challenge on Perceptual Image Quality Assessment

Cited by 35 publications

References 59 publications

Image Quality Assessment with Gradient Siamese Network

Image Quality Assessment with Gradient Siamese Network

NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Challenge on Night Photography Rendering

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