Region-Adaptive Deformable Network for Image Quality Assessment

Shi, Shuwei; Bai, Qingyan; Cao, Mingdeng; Xia, Weihao; Wang, Jiahao; Chen, Yifan; Yang, Yujiu

doi:10.1109/cvprw53098.2021.00042

Cited by 27 publications

(11 citation statements)

References 28 publications

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“…Similar to refs. [1–39], we use two widely used metrics to measure the prediction accuracy of IQA models. One is the Pearson linear correlation coefficient (PLCC).…”

Section: Methodsmentioning

confidence: 99%

CPDINet: Blind image quality assessment via a content perception and distortion inference network

Shao

Liu

Zhang

2022

IET Image Processing

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Nowadays, it is still challenging for the blind image quality assessment (BIQA) to accurately predict quality scores of distorted images, since distorted images have rich content information and complex distortions. To solve these problems, a content perception and distortion inference network for BIQA is proposed, which divides IQA task into content perception and distortion inference processes. Since humans try to understand image content before perceiving quality scores, a content feature extractor is designed to explore content information in an image to deal with the content variation problem. To handle the distortion diversity problem, a distortion feature extractor is proposed to capture distortion features in images. Because extracted content features and distortion ones have different characteristics, attention-based fusion blocks to fuse multi-scale content features and distortion ones as guidance to selectively enhance important features based on calculated weight scores are proposed. With fused features, a quality prediction module is designed to regress multi-scale features to quality scores. Experiments are performed on six public IQA datasets, including LIVE, CSIQ, TID2013, LIVEC, KonIQ-10k, and SPAQ. Experimental results show that the proposed method can effectively predict quality scores for both synthetically and authentically distorted images than its peers, including the state-of-the-art methods.

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“…Similar to refs. [1–39], we use two widely used metrics to measure the prediction accuracy of IQA models. One is the Pearson linear correlation coefficient (PLCC).…”

Section: Methodsmentioning

confidence: 99%

CPDINet: Blind image quality assessment via a content perception and distortion inference network

Shao

Liu

Zhang

2022

IET Image Processing

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“…They have used a pretrained Inception-Resnet-v2 network [25] as feature extraction backbone and transformer encoder-decoder architecture to obtain quality score predidction. Shi et al [23] proposed Region-Adaptive Deformable Network which uses reference-oriented deformable convolution to improve performance of the net-work on GAN-based distortion by adaptively accounting spatial misalignment. Their patch-level attention module contributes to enhance the interaction between distinct patch regions that were previously processed separately.…”

Section: Related Workmentioning

confidence: 99%

Can No-reference features help in Full-reference image quality estimation?

Dutta¹,

Das²,

Shah³

2022

Preprint

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Development of perceptual image quality assessment (IQA) metrics has been of significant interest to computer vision community. The aim of these metrics is to model quality of an image as perceived by humans. Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction. However, pixelwise feature comparison may not be meaningful if distortion present in query image is severe. In this context, we explore utilization of no-reference features in Full-reference IQA task. Our model consists of both full-reference and noreference branches. Full-reference branches use both distorted and reference images, whereas No-reference branch only uses distorted image. Our experiments show that use of no-reference features boosts performance of image quality assessment. Our model achieves higher SRCC and KRCC scores than a number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.

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“…Similar to other computer vision problems, advanced data-driven methods have also motivated the investigation of applications of IQA, such as LPIPS [80], PieAPP [54], WaDIQaM [9], SDW [28] and DISTS [19]. The 2021 NTIRE challenge has also brought some excellent FR-IQA methods, i.e., Cheon et al [13] propose a transformer-based FR-IQA method IQT and win the first place at the challenge, Guo et al [33] propose bilateral-branch multi-scale image quality estimation (IQMA) network, and Shi et al [59] propose Region Adaptive Deformable Network (RADN).…”

Section: Related Workmentioning

confidence: 99%

“…They also adopt the ensemble method to improve the performance of the final method. Their final submission is an ensemble of the proposed IQA Conformer Network and two pre-trained models: RADN [59], and ASNA [3].…”

Section: Jmu-cvlabmentioning

confidence: 99%

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

Gu¹,

Cai²,

Chen³

et al. 2022

Preprint

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This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate stateof-the-art performance.

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Region-Adaptive Deformable Network for Image Quality Assessment

Cited by 27 publications

References 28 publications

CPDINet: Blind image quality assessment via a content perception and distortion inference network

CPDINet: Blind image quality assessment via a content perception and distortion inference network

Can No-reference features help in Full-reference image quality estimation?

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

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