268 citations · 516 across the 13 of their papers we have counts for
16 papers
DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation
Jiahao Zhan, Yan Wang, Yongrui Ma +5
Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video teacher's approximation of t…
MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos
Jiahao Zhan, Yongrui Ma, Qunliang Xing +6
Despite rapid progress in video generation models, they still exhibit obvious motion deficiencies, often manifested as incorrect object motion. However, most existing video quality…
VQ-Jarvis: Retrieval-Augmented Video Restoration Agent with Sharp Vision and Fast Thought
Xuanyu Zhang, Weiqi Li, Qunliang Xing +6
Video restoration in real-world scenarios is challenged by heterogeneous degradations, where static architectures and fixed inference pipelines often fail to generalize. Recent age…
OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution
Shijie Zhao, Xuanyu Zhang, Bin Chen +8
Aligning generative real-world image super-resolution models with human visual preference is challenging due to the perception--fidelity trade-off and diverse, unknown degradations…
Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations
Jing Yang, Qunliang Xing, Mai Xu +1
Joint Photographic Experts Group (JPEG) achieves data compression by quantizing Discrete Cosine Transform (DCT) coefficients, which inevitably introduces compression artifacts. Mos…
Breaking the Multi-Enhancement Bottleneck: Domain-Consistent Quality Enhancement for Compressed Images
Qunliang Xing, Mai Xu, Jing Yang +1
Quality enhancement methods have been widely integrated into visual communication pipelines to mitigate artifacts in compressed images. Ideally, these quality enhancement methods s…
MarsSQE: Stereo Quality Enhancement for Martian Images Using Bi-level Cross-view Attention
Mai Xu, Yinglin Zhu, Qunliang Xing +2
Stereo images captured by Mars rovers are transmitted after lossy compression due to the limited bandwidth between Mars and Earth. Unfortunately, this process results in undesirabl…
MarsQE: Semantic-Informed Quality Enhancement for Compressed Martian Image
Chengfeng Liu, Mai Xu, Qunliang Xing +1
Lossy image compression is essential for Mars exploration missions, due to the limited bandwidth between Earth and Mars. However, the compression may introduce visual artifacts tha…
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
Qunliang Xing, Mai Xu, Shengxi Li +4
Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a…
DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus
Qunliang Xing, Mai Xu, Xin Deng +1
Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, existing qu…
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Meisong Zheng +75
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the…
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Yingqian Wang +3
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new sol…
Progressive Training of A Two-Stage Framework for Video Restoration
Meisong Zheng, Qunliang Xing, Minglang Qiao +4
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. Among v…
Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
Qunliang Xing, Mai Xu, Tianyi Li +1
Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. Recently, extensive approaches have been proposed…
DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC
Tianyi Li, Mai Xu, Runzhi Tang +2
Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its ancestor standard High Efficiency Video Coding (HEVC), but at the expens…
MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Qunliang Xing, Zhenyu Guan, Mai Xu +3
The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the qu…