检索结果-内蒙古大学图书馆

Compressed Video Quality Metric Based on Just-Noticeable-Difference and Saliency-aware Blocking Detection 7

学校读者我要写书评

暂无评论

Compressed Video Quality Metric Based on Just-Noticeable-Dif...

7th International Conference on Computer and Communications, ICCC 2021

作者： Wang, Zheng Liao, Guodong Wu, Yuxuan Lin, Liqun Yang, Jing Zheng, Haifeng Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Huawei Technologies Co.Ltd. Guangdong Shenzhen China

ISBN: (纸本)9781665409506

Block-based prediction-quantization hybrid coding framework is widely used in video compression, which results in visually annoying artifacts, i.e., blocking artifacts, on encoded videos. They significantly degrade the Quality-of-Experience (QoE). To improve the QoE, it is crucial to develop Video Quality Assessment (VQA) methods that can identify and quantify various artifacts, among which the negative effect introduced by blocking artifacts is of guiding significance. To evaluate and monitor video quality effectively, a novel Blocking-based Compressed Video Quality Assessment (BCVQA) is proposed in this paper. The key idea of the BCVQA method is a blocking detection model based on Just-Noticeable-Difference (JND) mechanism, which is aimed to improve detection performance, since it only focuses on the observable blocking artifacts. Furthermore, the saliency model is utilized in quality prediction procedure after blocking detection, considering the visual attention mechanism. With the fusion of the above two visual mechanisms, quality prediction of compressed videos becomes more effective, since the useful information is refined in our framework. Experimental results show that the proposed BCVQA is consistent with perceived quality and can effectively evaluate video quality. © 2021 IEEE.

关键词： Image compression

MSFEN-AM: A Non-intrusive Load Identification Method for Power Saving

学校读者我要写书评

暂无评论

MSFEN-AM: A Non-intrusive Load Identification Method for Pow...

International Conference on Information, Communication and Networks (ICICN)

作者： Yiwen Xu Dengfeng Liu Zhiquan Lin Tiesong Zhao Nian He Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Zhicheng College Fuzhou University Fuzhou China Peng Cheng Laboratory Shenzhen China

关键词：

λ-domain VVC Rate Control Based on Game Theory

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Lin, Jielian Huang, Aiping Zhang, Keke Wang, Xu Zhao, Tiesong Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Versatile Video Coding (VVC) has set a new milestone in high-efficiency video coding. In the standard encoder, the λ-domain rate control is incorporated for its high accuracy and good Rate-Distortion (RD) performance. In this paper, we formulate this task as a Nash equilibrium problem that effectively bargains between multiple agents, i.e., Coding Tree Units (CTUs) in the frame. After that, we calculate the optimal λ value with a two-step strategy: a Newton method to iteratively obtain an intermediate variable, and a solution of Nash equilibrium to obtain the optimal λ. Finally, we propose an effective CTU-level rate allocation with the optimal λ value. To the best of our knowledge, we are the first to combine game theory with λ-domain rate control. Experimental results with Common Test Conditions (CTC) demonstrate the efficiency of the proposed method, which outperforms the state-of-the-art CTU-level rate allocation algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Lin, Junhong Jiang, Nanfeng Zhang, Zhentao Chen, Weiling Zhao, Tiesong The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China The Peng Cheng Laboratory Shenzhen518055 China

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a LaplaceVQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-the-art snow removal quality with significantly reduced parameters and the lowest running time. Codes and models are available at https://***/StephenLinn/LMQFormer. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhao, Tiesong Huang, Yuhang Feng, Weize Xu, Yiwen Kwong, Sam The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China The Peng Cheng Laboratory Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatial-temporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences. Copyright © 2022, The Authors. All rights reserved.

关键词： Video signal processing

Compressed Video Quality Metric Based on Just-Noticeable-Difference and Saliency-aware Blocking Detection

学校读者我要写书评

暂无评论

Compressed Video Quality Metric Based on Just-Noticeable-Dif...

International Conference on Computer and Communications (ICCC)

作者： Zheng Wang Guodong Liao Yuxuan Wu Liqun Lin Jing Yang Haifeng Zheng Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Huawei Technologies Co.Ltd. Shenzhen Guangdong China

ISBN: (纸本)9781665409513

关键词： visualization Redundancy Video compression Predictive models Quality assessment Quality of experience Video recording

Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

Research on Video Motion Characteristics Extraction and Description Based on Human visual Characteristics 2

学校读者我要写书评

暂无评论

Research on Video Motion Characteristics Extraction and Desc...

2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Ying Zhou Yongsheng Liang Wei Liu Lixia Zhao Shenzhen Key Laboratory of Visual Media Processing and Transmission Shenzhen Institute of Information Technology

With the rapid development of Internet and multimedia, there is new challenge on streaming media processing and transmission. On one hand, the demand for the quantity and quality of multimedia resourc

ISBN: (纸本)9781467389808

关键词： visualization Streaming media Video sequences visual perception Market research Standards Complexity theory