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学者姓名：徐艺文
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Abstract ：
Rain streaks typically cause significant visual degradation and foreground occlusions, hindering the progress of visual tasks in outdoor scenarios. Existing image deraining methods, predominantly based on Convolutional Neural Networks (CNNs), exhibit certain limitations. These methods tend to overly focus on low-level visual features, demonstrating insufficient ability to capture high-dimensional global features. Furthermore, they often lack targeted attention to channel information and spatial details, which restricts their effectiveness. To address these shortcomings, this paper proposes the Delta-Calibration Derain Network (DCD-Net). The DCD-Net introduces a sequential Delta Convolutional Layer structure to significantly expand the feature acquisition range. Additionally, this study pioneers the Joint Calibration Attention module, which precisely captures both channel and spatial feature information, leading to enhanced network performance. Experimental results across multiple synthetic datasets show that the proposed method achieves superior performance in terms of Peak Signal-to-Noise Ratio and Structural Similarity Index, validating the advantages of DCD-Net over traditional CNN-based models.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Image deraining Image deraining Image processing Image processing
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| GB/T 7714 | Que, Hanjing , Weng, Jianing , Fang, Ying et al. DCD-Net: image deraining with delta convolution and joint calibration attention [J]. | SIGNAL IMAGE AND VIDEO PROCESSING , 2025 , 19 (1) . |
| MLA | Que, Hanjing et al. "DCD-Net: image deraining with delta convolution and joint calibration attention" . | SIGNAL IMAGE AND VIDEO PROCESSING 19 . 1 (2025) . |
| APA | Que, Hanjing , Weng, Jianing , Fang, Ying , Hu, Kejian , Wei, Hongan , Xu, Yiwen . DCD-Net: image deraining with delta convolution and joint calibration attention . | SIGNAL IMAGE AND VIDEO PROCESSING , 2025 , 19 (1) . |
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The recent advances in multimedia technology have significantly expanded the range of audio-visual applications. The continuous enhancement of display quality has led to the emergence of new attributes in video, such as enhanced visual immersion and widespread availability. Within media content, the video signals are presented in various formats including stereoscopic/3D, panoramic/360 degrees degrees and holographic images. The signals are also combined with other sensory elements, such as audio, tactile, and olfactory cues, creating a comprehensive multi-sensory experience for the user. The development of both qualitative and quantitative Quality of Experience (QoE) metrics is crucial for enhancing the subjective experience in immersive scenarios, providing valuable guidelines for system enhancement. In this paper, we review the most recent achievements in QoE assessment for immersive scenarios, summarize the current challenges related to QoE issues, and present outlooks of QoE applications in these scenarios. The aim of our overview is to offer a valuable reference for researchers in the domain of multimedia delivery.
Keyword ：
Immersive video Immersive video MULtiple SEnsorial MEDIA (MULSEMEDIA) MULtiple SEnsorial MEDIA (MULSEMEDIA) Quality of Experience (QoE) Quality of Experience (QoE) Video delivery Video delivery Video transmission Video transmission
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| GB/T 7714 | Chen, Weiling , Lan, Fengquan , Wei, Hongan et al. A comprehensive review of quality of experience for emerging video services [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
| MLA | Chen, Weiling et al. "A comprehensive review of quality of experience for emerging video services" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 128 (2024) . |
| APA | Chen, Weiling , Lan, Fengquan , Wei, Hongan , Zhao, Tiesong , Liu, Wei , Xu, Yiwen . A comprehensive review of quality of experience for emerging video services . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
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Within the domain of multimodal communication, the compression of audio, image, and video information is well-established, but compressing haptic signals, including vibrotactile signals, remains challenging. Particularly with the enhancement of haptic signal sampling rate and degrees of freedom, there is a substantial increase in data volume. While existing algorithms have made progress in vibrotactile codecs, there remains significant room for improvement in compression ratios. We propose an innovative Nbeats Network-based Vibrotactile Codec (NNVC) that leverages the statistical characteristics of vibrotactile data. This advanced codec integrates the Nbeats network for precise vibrotactile prediction, residual quantization, efficient Run-Length Encoding, and Huffman coding. The algorithm not only captures the intricate details of vibrotactile signals but also ensures high-efficiency data compression. It exhibits robust overall performance in terms of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR), significantly surpassing the state-of-the-art.
Keyword ：
Codecs Codecs Databases Databases Decoding Decoding Encoding Encoding Haptic interfaces Haptic interfaces haptics haptics Huffman coding Huffman coding Long short term memory Long short term memory Multimodal communication Multimodal communication PSNR PSNR Quantization (signal) Quantization (signal) signal compression signal compression Training Training vibrotactile vibrotactile
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| GB/T 7714 | Xu, Yiwen , Chen, Dongfang , Fang, Ying et al. Efficient Vibrotactile Codec Based on Nbeats Network [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 2845-2849 . |
| MLA | Xu, Yiwen et al. "Efficient Vibrotactile Codec Based on Nbeats Network" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 2845-2849 . |
| APA | Xu, Yiwen , Chen, Dongfang , Fang, Ying , Lu, Yang , Zhao, Tiesong . Efficient Vibrotactile Codec Based on Nbeats Network . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 2845-2849 . |
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Integrating haptic feedback with audio and video not only expands the perceptual dimensions of multimedia applications but also enhances user engagement and experience. However, higher signal sampling rates and multi-degree-freedom in haptic interaction increase data significantly. For low-latency and reliable transmission of haptic signal (i.e. tactile and kinesthetic signals), efficient haptic coding is crucial. Existing algorithms overlook haptic signal characteristics, leaving room for improvement. We analyze the statistical characteristics of kinesthetic signals in-depth. Based on the local linear characteristics of position and velocity signals, and the sparse distribution of force signal, we propose an improved kinesthetic coding algorithm by combining dead-zone coding with segmented linear prediction. Extensive experiments on the standard datasets of the IEEE P1918.1.1 Haptic Codecs Task Group demonstrate the superior performance compared to state-of-the-art methods, achieving a more than halved reduction in data transmission rates with high signal-to-noise ratios and structural similarity IEEE
Keyword ：
Data compression Data compression Encoding Encoding Force Force Haptic interfaces Haptic interfaces Haptics Haptics Kinesthetic coding Kinesthetic coding Linear regression Linear regression Mathematical models Mathematical models Prediction algorithms Prediction algorithms Regression algorithm Regression algorithm Signal processing algorithms Signal processing algorithms
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| GB/T 7714 | Xu, Y. , Huang, Q. , Zheng, Q. et al. Perception-Based Prediction for Efficient Kinesthetic Coding [J]. | IEEE Signal Processing Letters , 2024 , 31 : 1-5 . |
| MLA | Xu, Y. et al. "Perception-Based Prediction for Efficient Kinesthetic Coding" . | IEEE Signal Processing Letters 31 (2024) : 1-5 . |
| APA | Xu, Y. , Huang, Q. , Zheng, Q. , Fang, Y. , Zhao, T. . Perception-Based Prediction for Efficient Kinesthetic Coding . | IEEE Signal Processing Letters , 2024 , 31 , 1-5 . |
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With the burgeoning growth of the e-sports industry, there has been a rapid proliferation of gaming videos on online platforms. Simultaneously, this surge undoubtedly presents significant challenges to the encoding of game videos. However, the recurring characteristic of gaming videos is not efficiently studied in the current standardization, such as Versatile Video Coding (VVC). Based on these observations, our general framework, utilizing the Structural SIMilarity index metric (SSIM), Hash SIMilarity index metric (HSIM), and Hash Matrix SIMilarity index metric (HMSIM), identifies recurring video clips and adjusts the reference frame of the first frame. Additionally, we analyze recurring patterns in different resolutions and types, proposing our Scene Adaptation (SA) optimization algorithm, which integrates SSIM, HSIM, and HMSIM to adapt to various resolutions and scenes. Experimental results show that the proposed approach can achieve a -4.71 % Bjontegaard Delta Bit Rate (BDBR) and 1.00% Time Save (TS), outperforming the benchmark. © 2024 IEEE.
Keyword ：
component component Gaming Video Coding Gaming Video Coding Recurring Characteristic Recurring Characteristic Scene Adaptation Scene Adaptation Versatile Video Coding Versatile Video Coding
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| GB/T 7714 | Lin, C. , Lin, S. , Fang, Y. et al. Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding [未知]. |
| MLA | Lin, C. et al. "Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding" [未知]. |
| APA | Lin, C. , Lin, S. , Fang, Y. , Yi, S. , Wei, H. , Xu, Y. . Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding [未知]. |
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State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.
Keyword ：
computer vision computer vision electric load monitoring electric load monitoring load recognition algorithm load recognition algorithm smart city smart city smart electric energy management smart electric energy management
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| GB/T 7714 | He, Nian , Liu, Dengfeng , Zhang, Zhichen et al. Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management [J]. | SENSORS , 2024 , 24 (10) . |
| MLA | He, Nian et al. "Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management" . | SENSORS 24 . 10 (2024) . |
| APA | He, Nian , Liu, Dengfeng , Zhang, Zhichen , Lin, Zhiquan , Zhao, Tiesong , Xu, Yiwen . Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management . | SENSORS , 2024 , 24 (10) . |
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Nowadays, Versatile Video Coding (VVC) has achieved a superior performance than previous video coding standard (High Efficiency Video Coding). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can enhance the coding performance. Nevertheless, this technique also leads to the significantly increasing complexity of VVC inter coding. Therefore, complexity optimization is an urgent problem to be optimized in the market application of VVC. To solve this issue, we propose a Supervised-Contrastive-Learningbased Inter Partitioning (SCLIP) method in this paper. Firstly, we define the above complexity optimization problem as a supervised classification task. Next, we develop a SCLIP Estimation Network (SCLIPEst-Net) with a supervised contrastive learning module and a classification module. After training on a newly established dataset, the SCLIPEst-Net can reasonably predict the mode partitioning. Finally, we propose an overall SCLIP algorithm that effectively determines the inter partitions of VVC with a low computational overhead. Experimental results indicate that our method achieves 45.14% average Time Saving (TS) with a 2.40% Bj & oslash;ntegaard Delta Bit Rate (BDBR) in Random Access (RA), outperforming the benchmarks.
Keyword ：
Complexity optimization Complexity optimization Inter prediction Inter prediction Supervised contrastive learning Supervised contrastive learning Versatile video coding Versatile video coding
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| GB/T 7714 | Lin, JieLian , Lin, Hongbin , Zhang, Zhichen et al. Efficient inter partitioning of versatile video coding based on supervised contrastive learning [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 296 . |
| MLA | Lin, JieLian et al. "Efficient inter partitioning of versatile video coding based on supervised contrastive learning" . | KNOWLEDGE-BASED SYSTEMS 296 (2024) . |
| APA | Lin, JieLian , Lin, Hongbin , Zhang, Zhichen , Xu, Yiwen . Efficient inter partitioning of versatile video coding based on supervised contrastive learning . | KNOWLEDGE-BASED SYSTEMS , 2024 , 296 . |
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针对相控阵列辅助的无线通信系统中发射波束只依赖角度特性而导致的安全隐患问题，以及传统的迭代算法所带来的高计算复杂度问题，该文提出由深度学习(DL)和随机频率分集阵列(RFDA)辅助带有3维安全区域的安全传输方案。首先，推导在3维空间中带有安全区域的期望用户实现安全通信的传输条件。在此基础上，构建系统安全速率下界最大化问题。随后，提出基于深度学习的神经网络方案来设计最优的波束成形矢量和人工噪声(AN)矢量来降低计算复杂度。仿真结果表明：即便是在窃听者位于安全区域边缘的最差情况下，所提方案仍能够在实现3维安全传输，能够保证安全区域内接收到的信息不被窃听。
Keyword ：
安全传输 安全传输 深度学习 深度学习 随机频率分集阵列 随机频率分集阵列
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| GB/T 7714 | 胡锦松 , 蒋宛伶 , 陈由甲 et al. 深度学习辅助的随机频率分集阵列下的三维无线安全传输 [J]. | 电子与信息学报 , 2023 , 45 (06) : 2063-2070 . |
| MLA | 胡锦松 et al. "深度学习辅助的随机频率分集阵列下的三维无线安全传输" . | 电子与信息学报 45 . 06 (2023) : 2063-2070 . |
| APA | 胡锦松 , 蒋宛伶 , 陈由甲 , 徐艺文 , 赵铁松 , 束锋 . 深度学习辅助的随机频率分集阵列下的三维无线安全传输 . | 电子与信息学报 , 2023 , 45 (06) , 2063-2070 . |
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Emerging multimedia technologies significantly enhance the naturalness and immersion of human computer interaction. Currently, research on kinesthetic information has gained increasing attentions of multimedia community. However, effective interaction between kinesthetic and other multimedia signals remains a challenging task. In this paper, we propose a visual-kinesthetic interaction in Virtual Reality (VR) and real-world control tasks. First, we model the correlation between user's visual attention and kinesthetic positions under different tasks. Second, we utilize an attention-based Long Short-Term Memory network to predict the kinesthetic positions. Third, we build a VR system with robotic car control, which validates our model in VR interaction and control tasks. With a high task achievement rate, we envision the implementation of kinesthetic information in a more natural interaction system. The VR interaction system based on the proposed model can also provide guidance for the design of immersive robot teleoperation systems.
Keyword ：
Haptics Haptics Human-Computer Interaction (HCI) Human-Computer Interaction (HCI) Multimedia Multimedia Virtual Reality (VR) Virtual Reality (VR) Visual attention Visual attention
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| GB/T 7714 | Fang, Ying , Liu, Qian , Xu, Yiwen et al. Virtual reality interaction based on visual attention and kinesthetic information [J]. | VIRTUAL REALITY , 2023 , 27 (3) : 2183-2193 . |
| MLA | Fang, Ying et al. "Virtual reality interaction based on visual attention and kinesthetic information" . | VIRTUAL REALITY 27 . 3 (2023) : 2183-2193 . |
| APA | Fang, Ying , Liu, Qian , Xu, Yiwen , Guo, Yanmin , Zhao, Tiesong . Virtual reality interaction based on visual attention and kinesthetic information . | VIRTUAL REALITY , 2023 , 27 (3) , 2183-2193 . |
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为了精确地传输信号内容含义,实现智能识别与信号重建,针对振动触觉信号,提出了一种面向机器识别?人类感知的联合编码方案.在编码端,将三维振动信号转化为一维信号,采用短时傅里叶变换提取信号的语义信息,并实现语义信息高效压缩与表征.在解码端,基于语义信息采用全卷积神经网络实现触觉的智能识别;同时,将原始信号与基于语义信息的重构信号的残差值作为语义信息的补偿,逐步提高重构信号的质量,满足人类感知需求.实验结果表明,所提方案用较低比特率的语义信息实现触觉识别,同时在满足人类感知需求情况下,触觉数据的压缩效率有所提高.
Keyword ：
感知质量 感知质量 振动触觉 振动触觉 智能识别 智能识别 联合编码 联合编码 触觉 触觉 语义信息 语义信息
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| GB/T 7714 | 房颖 , 徐艺文 , 赵铁松 . 面向机器识别−人类感知的联合振动触觉编码 [J]. | 通信学报 , 2023 , 44 (5) : 42-51 . |
| MLA | 房颖 et al. "面向机器识别−人类感知的联合振动触觉编码" . | 通信学报 44 . 5 (2023) : 42-51 . |
| APA | 房颖 , 徐艺文 , 赵铁松 . 面向机器识别−人类感知的联合振动触觉编码 . | 通信学报 , 2023 , 44 (5) , 42-51 . |
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