MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Qunliang Xing, Zhenyu Guan, Mai Xu, Ren Yang, Tie Liu, Zulin Wang
备注：
Accepted to TPAMI in September, 2019. v6 updates: correct units in Fig. 11; correct author info; delete bio photos. arXiv admin note: text overlap with arXiv:1803.04680
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 quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we firstly develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code is available at https://github.com/RyanXingQL/MFQEv2.0.git.
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network
Xidian University（西安电子科技大学）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；BBC Research & Development（英国广播公司研发部）；Northwestern Polytechnical University（西北工业大学）
Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model
Faculty of Electronics and Telecommunications University Of Engineering and Technology（电子与电信学院工程科技大学）；Vietnam National University, Hanoi（越南国家大学河内分校）；Department of Computer Science National Yang Ming Chiao Tung University（计算机科学系阳明交通大学）
Compressed Video Quality Enhancement: Classifying and Benchmarking over Standards
Faculty of Electronics and Telecommunications, VNU University of Engineering and Technology（电子与电信学院，越南工程大学）；Department of Computer Science, National Yang Ming Chiao Tung University（计算机科学系，国家阳明交通大学）
MGANet: A Robust Model for Quality Enhancement of Compressed Video
The Hong Kong University of Science and Technology（香港科技大学）；University of Electronic Science and Technology of China（电子科技大学）；Megvii Inc（旷视科技）；Tencent Research（腾讯研究院）
Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement
Hangzhou Dianzi University（杭州电子科技大学）；State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）；Xinhua Zhiyun Technology Co., Ltd.（新华智云科技有限公司）
Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference
School of Computer Science, Nanjing University of Information Science & Technology（南京信息工程大学计算机科学学院）；Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)（江苏省大气环境与装备技术协同创新中心）；School of Software, Nanjing University of Information Science & Technology（南京信息工程大学软件学院）；department of Intelligent Science, Xi’an Jiaotong-Liverpool University（智能科学系，西安交通大学利物浦大学）；Department of Computing Sciences, Tampere University（计算科学系，塔尔基大学）；School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）
DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（教育部机器智能与系统控制重点实验室）；Department of Computing and Decision Science, Lingnan University（岭南大学计算与决策科学系）
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement
State Key Laboratory of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；National Computer Network Emergency Response Technical Team, Coordination Center of China（中国国家计算机网络应急技术处理协调中心）
STQE: Spatial-Temporal Attribute Quality Enhancement for G-PCC Compressed Dynamic Point Clouds
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（机器智能与系统控制重点实验室，教育部）；School of Software, Shandong University（软件学院，山东大学）；School of Engineering and Sustainable Development, De Montfort University（工程与可持续发展学院，德蒙福特大学）；Department of Computing and Decision Science, Lingnan University（计算与决策科学系，岭南大学）
UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（机器智能与系统控制重点实验室，教育部）；School of Engineering and Sustainable Development, De Montfort University（工程与可持续发展学院，德蒙福特大学）；Institute of NUIST-MetaX for Graphics Processing Unit, Nanjing University of Information Science and Technology（信息科学技术大学NUIST-MetaX图形处理单元研究所）
Bit-depth enhancement detection for compressed video
Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；MSU Institute of Advanced Studies of Artificial Intelligence and Intelligent Systems（莫斯科国立大学人工智能与智能系统高等研究院）
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
University of Science and Technology Beijing（北京科技大学）；Microsoft Research（微软研究院）；Shunde Graduate School of University of Science and Technology Beijing（北京科技大学顺德研究生院）
Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement
Shenzhen Polytechnic University（深圳职业技术大学）；The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Chu Hai College（香港珠海学院）；Dongguan University of Technology（东莞理工学院）；City University of Hong Kong（香港城市大学）
