Search Results for author: Jianzhao Liu

Found 5 papers, 1 papers with code

StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment

no code implementations29 Jul 2022 Yiting Lu, Xin Li, Jianzhao Liu, Zhibo Chen

Specifically, we find a more compact and reliable space i. e., feature style space for perception-oriented UDA based on an interesting/amazing observation, that the feature style (i. e., the mean and variance) of the deep layer in DNNs is exactly associated with the quality score in NR-IQA.

Image Quality Assessment NR-IQA +1

Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

no code implementations17 Jul 2022 Jianzhao Liu, Xin Li, Shukun An, Zhibo Chen

Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA.

Blind Image Quality Assessment Unsupervised Domain Adaptation

SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

1 code implementation9 May 2022 Jianzhao Liu, Xin Li, Yanding Peng, Tao Yu, Zhibo Chen

In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.

Compressed Image Quality Assessment Image Compression +1

FAN: Frequency Aggregation Network for Real Image Super-resolution

no code implementations30 Sep 2020 Yingxue Pang, Xin Li, Xin Jin, Yaojun Wu, Jianzhao Liu, Sen Liu, Zhibo Chen

Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.

Image Super-Resolution SSIM

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

no code implementations ECCV 2020 Jianzhao Liu, Jianxin Lin, Xin Li, Wei Zhou, Sen Liu, Zhibo Chen

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.

Image Restoration SSIM

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