Search Results for author: Qunliang Xing

Found 7 papers, 6 papers with code

Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

no code implementations27 Feb 2024 Qunliang Xing, Mai Xu, Shengxi Li, Xin Deng, Meisong Zheng, Huaida Liu, Ying Chen

However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain.

DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus

1 code implementation20 Nov 2022 Qunliang Xing, Mai Xu, Xin Deng, Yichen Guo

Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality.

Progressive Training of A Two-Stage Framework for Video Restoration

2 code implementations21 Apr 2022 Meisong Zheng, Qunliang Xing, Minglang Qiao, Mai Xu, Lai Jiang, Huaida Liu, Ying Chen

As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts.

Transfer Learning Video Restoration +2

Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images

1 code implementation ECCV 2020 Qunliang Xing, Mai Xu, Tianyi Li, Zhenyu Guan

Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming.

Image Enhancement Image Restoration

DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC

1 code implementation23 Jun 2020 Tianyi Li, Mai Xu, Runzhi Tang, Ying Chen, Qunliang Xing

In VVC, the quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization.

MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

1 code implementation26 Feb 2019 Qunliang Xing, Zhenyu Guan, Mai Xu, Ren Yang, Tie Liu, Zulin Wang

Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.

Video Enhancement Video Restoration

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