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）
In recent years, display intensity and contrast have increased considerably. Many displays support high dynamic range (HDR) and 10-bit color depth. Since high bit-depth is an emerging technology, video content is still largely shot and transmitted with a bit depth of 8 bits or less per color component. Insufficient bit-depths produce distortions called false contours or banding, and they are visible on high contrast screens. To deal with such distortions, researchers have proposed algorithms for bit-depth enhancement (dequantization). Such techniques convert videos with low bit-depth (LBD) to videos with high bit-depth (HBD). The quality of converted LBD video, however, is usually lower than that of the original HBD video, and many consumers prefer to keep the original HBD versions. In this paper, we propose an algorithm to determine whether a video has undergone conversion before compression. This problem is complex; it involves detecting outcomes of different dequantization algorithms in the presence of compression that strongly affects the least-significant bits (LSBs) in the video frames. Our algorithm can detect bit-depth enhancement and demonstrates good generalization capability, as it is able to determine whether a video has undergone processing by dequantization algorithms absent from the training dataset.
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）
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）
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）
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）
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）
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）
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）
