IEEE Transactions on Broadcasting最新文献_第3页

Localization With DTMB Signal Under Complex Urban Environments 复杂城市环境下DTMB信号定位

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-04-08 DOI: 10.1109/TBC.2025.3549994

Tao Zhou;Liang Chen;Jing Sun;Zhenghang Jiao

{"title":"Localization With DTMB Signal Under Complex Urban Environments","authors":"Tao Zhou;Liang Chen;Jing Sun;Zhenghang Jiao","doi":"10.1109/TBC.2025.3549994","DOIUrl":"https://doi.org/10.1109/TBC.2025.3549994","url":null,"abstract":"Digital multimedia broadcast (DTMB) signal presents a potential opportunity for wireless localization. This paper studies the time of arrival (TOA) estimation based on the DTMB signal for localization. Theoretical analysis of the autocorrelation on the DTMB signal suggested that the DTMB signal has the characteristics for localization. In this paper, we propose software-defineded radio (SDR) receiver based on the DTMB signal for localization. The key innovations of the proposed SDR receiver are as follows: 1) employing a narrow Early-Minus-Late Power Delay Discriminator (nEML) in the delay-locked loop (DLL) to improve the multipath resistance; 2) proposing a multi-state fusion filter to improve the robustness and accuracy of the loop filter; 3) utilizing the carrier-to-noise radio (C/N0) to remove the range observation influenced by heavy non-line of sight (NLOS) environment, thereby reducing the impact of low-quality observations. The static field experiments show that the accuracy of TOA ranging is 1.666m. The motion experiment results show that the root mean square error (RMSE) of the TOA measurements from the DTMB receiver is about 16m, and the RMSE of the DTMB localization is about 17.7m, which shows that the designed receiver can provide relatively reliable localization results when processing DTMB signal in complex urban environments.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"439-452"},"PeriodicalIF":3.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Recent Advances in Video Coding Technologies and Future Research Directions 视频编码技术的最新进展及未来研究方向

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-04-04 DOI: 10.1109/TBC.2025.3553306

Houbang Guo;Yun Zhou;Hongwei Guo;Zhuqing Jiang;Tian He;Yiyan Wu

引用次数: 0

Low-Complexity Patch-Based No-Reference Point Cloud Quality Metric Exploiting Weighted Structure and Texture Features 基于加权结构和纹理特征的低复杂度补丁无参考点云质量度量

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-04-03 DOI: 10.1109/TBC.2025.3553305

Michael Neri;Federica Battisti

{"title":"Low-Complexity Patch-Based No-Reference Point Cloud Quality Metric Exploiting Weighted Structure and Texture Features","authors":"Michael Neri;Federica Battisti","doi":"10.1109/TBC.2025.3553305","DOIUrl":"https://doi.org/10.1109/TBC.2025.3553305","url":null,"abstract":"During the compression, transmission, and rendering of point clouds, various artifacts are introduced, affecting the quality perceived by the end user. However, evaluating the impact of these distortions on the overall quality is a challenging task. This study introduces PST-PCQA, a no-reference point cloud quality metric based on a low-complexity, learning-based framework. It evaluates point cloud quality by analyzing individual patches, integrating local and global features to predict the Mean Opinion Score. In summary, the process involves extracting features from patches, combining them, and using correlation weights to predict the overall quality. This approach allows us to assess point cloud quality without relying on a reference point cloud, making it particularly useful in scenarios where reference data is unavailable. Experimental tests on three state-of-the-art datasets show good prediction capabilities of PST-PCQA, through the analysis of different feature pooling strategies and its ability to generalize across different datasets. The ablation study confirms the benefits of evaluating quality on a patch-by-patch basis. Additionally, PST-PCQA’s light-weight structure, with a small number of parameters to learn, makes it well-suited for real-time applications and devices with limited computational capacity. For reproducibility purposes, we made code, model, and pretrained weights available at <uri>https://github.com/michaelneri/PST-PCQA</uri>.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"631-640"},"PeriodicalIF":3.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frame-Channel Polarization for Improved Reliability in Mobile Video Wireless Transmission 提高移动视频无线传输可靠性的帧信道极化

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-04-02 DOI: 10.1109/TBC.2025.3549991

Zhaoyang Wang;Jiaxi Zhou;Guanghua Liu;Yangyang Liu;Ting Bi;Tao Jiang

{"title":"Frame-Channel Polarization for Improved Reliability in Mobile Video Wireless Transmission","authors":"Zhaoyang Wang;Jiaxi Zhou;Guanghua Liu;Yangyang Liu;Ting Bi;Tao Jiang","doi":"10.1109/TBC.2025.3549991","DOIUrl":"https://doi.org/10.1109/TBC.2025.3549991","url":null,"abstract":"In this paper, we propose a Frame-Channel Polarization (FCP) technique to enhance wireless transmission reliability for low-latency mobile video in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. We begin by analyzing the reliability of video frame transmission, quantified by the Transmission Success Probability (TSP), and derive closed-form TSP expressions under Maximum Ratio Combining (MRC) for a single subcarrier. We also summarize the corresponding TSP formulation for Zero-Forcing (ZF). To extend the analysis to multiple subcarriers, we introduce a dynamic programming approach that computes the TSP for multiple subcarriers based on the single-subcarrier results, thereby reducing computational complexity from exponential to polynomial. Using TSP as a reliability metric, the FCP method dynamically prioritizes subcarrier allocation, assigning more resources to high-priority video frames while allocating fewer subcarriers to lower-priority frames. As a result, the reliability of frame channels becomes polarized, with the degree of polarization directly linked to the reliability requirements of each frame. Experimental results validate the accuracy of the derived TSP expressions for both single and multiple subcarriers and demonstrate that the FCP method significantly improves transmission reliability compared to existing methods, achieving improvements in reliability for low-latency video transmission.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"467-479"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VaVLM: Toward Efficient Edge-Cloud Video Analytics With Vision-Language Models VaVLM：基于视觉语言模型的高效边缘云视频分析

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-04-02 DOI: 10.1109/TBC.2025.3549983

Yang Zhang;Hanling Wang;Qing Bai;Haifeng Liang;Peican Zhu;Gabriel-Miro Muntean;Qing Li

{"title":"VaVLM: Toward Efficient Edge-Cloud Video Analytics With Vision-Language Models","authors":"Yang Zhang;Hanling Wang;Qing Bai;Haifeng Liang;Peican Zhu;Gabriel-Miro Muntean;Qing Li","doi":"10.1109/TBC.2025.3549983","DOIUrl":"https://doi.org/10.1109/TBC.2025.3549983","url":null,"abstract":"The advancement of Large Language Models (LLMs) with vision capabilities in recent years has elevated video analytics applications to new heights. To address the limited computing and bandwidth resources on edge devices, edge-cloud collaborative video analytics has emerged as a promising paradigm. However, most existing edge-cloud video analytics systems are designed for traditional deep learning models (e.g., image classification and object detection), where each model handles a specific task. In this paper, we introduce VaVLM, a novel edge-cloud collaborative video analytics system tailored for Vision-Language Models (VLMs), which can support multiple tasks using a single model. VaVLM aims to enhance the performance of VLM-powered video analytics systems in three key aspects. First, to reduce bandwidth consumption during video transmission, we propose a novel Region-of-Interest (RoI) generation mechanism based on the VLM’s understanding of the task and scene. Second, to lower inference costs, we design a task-oriented inference trigger that processes only a subset of video frames using an optimized inference logic. Third, to improve inference accuracy, the model is augmented with additional information from both the environment and auxiliary analytics models during the inference stage. Extensive experiments on real-world datasets demonstrate that VaVLM achieves an 80.3% reduction in bandwidth consumption and an 89.5% reduction in computational cost compared to baseline methods.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"529-541"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced Spectrum Sharing Techniques for Coexistence of OFDM Radar and 5G BMSB System OFDM雷达与5G BMSB共存的先进频谱共享技术

IF 4.8 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-04-01 DOI: 10.1109/TBC.2025.3553298

Rongxing Guo;Junsheng Mu;Jia Zhu;Lei Liu;Fei Qi;Yi Wang

引用次数: 0

A Live Adaptive Streaming Solution for Enhancing Quality of Experience in Co-Created Opera 一种实时自适应流媒体解决方案，用于提高共同创作Opera的体验质量

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-31 DOI: 10.1109/TBC.2025.3541875

Rohit Verma;Anderson Augusto Simiscuka;Mohammed Amine Togou;Mikel Zorrilla;Gabriel-Miro Muntean

{"title":"A Live Adaptive Streaming Solution for Enhancing Quality of Experience in Co-Created Opera","authors":"Rohit Verma;Anderson Augusto Simiscuka;Mohammed Amine Togou;Mikel Zorrilla;Gabriel-Miro Muntean","doi":"10.1109/TBC.2025.3541875","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541875","url":null,"abstract":"The collaborative nature of opera production offers a unique opportunity to strengthen societal cohesion and empower marginalized voices through storytelling. However, existing live streaming approaches, such as HTTP-Adaptive Streaming (HAS), are not equipped to handle the complexities of co-created opera content, resulting in suboptimal user experiences. To address these limitations, this article introduces the Live Stream Adaptation for Opera (LSAO), a solution designed as part of the EU Horizon 2020 TRACTION project. LSAO is a network-aware adaptive scheme designed to optimize the delivery of live co-created opera performances by dynamically adjusting audiovisual quality based on varying network conditions. Unlike traditional streaming solutions, LSAO prioritizes the unique demands of opera, ensuring seamless delivery and preserving artistic features. The evaluation of LSAO involved an online live opera show featuring four distinct performances by six artists located in globally distributed locations. Delivered to 35 remote viewers across 12 countries and 3 continents, the LSAO system was evaluated based on user feedback on the quality of their streaming experience. The results demonstrate the effectiveness of LSAO in enhancing audio and video quality levels, leading to heightened user enjoyment during live co-created opera performances. Through its approach and successful evaluation, LSAO represents a significant advancement in the delivery of live co-created opera content.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"480-491"},"PeriodicalIF":3.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Spectrum Sensing for TV White Space in 5G-MBMS Networks 5G-MBMS网络中基于深度学习的电视白空间频谱感知

IF 4.8 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-31 DOI: 10.1109/TBC.2025.3553296

Fenghua Xu;Yukun Zhu;Hongyuan Zhu;Junsheng Mu;Jie Wang;Bingxin Wang;Jieliang Zheng

{"title":"Deep Learning-Based Spectrum Sensing for TV White Space in 5G-MBMS Networks","authors":"Fenghua Xu;Yukun Zhu;Hongyuan Zhu;Junsheng Mu;Jie Wang;Bingxin Wang;Jieliang Zheng","doi":"10.1109/TBC.2025.3553296","DOIUrl":"https://doi.org/10.1109/TBC.2025.3553296","url":null,"abstract":"Accurate spectrum sensing in TV White Space (TVWS) is crucial for enhancing spectral efficiency in 5G Multimedia Broadcast Multicast Services (MBMS) networks. Traditional spectrum sensing techniques suffer from poor performance in low-SNR environments, necessitating a robust, data-driven approach. This study introduces a deep learning-based multi-feature fusion approach that integrates energy detection, cyclostationary analysis, and covariance matrix detection. The proposed model employs an adaptive thresholding mechanism and multi-task learning to enhance detection accuracy while ensuring real-time feasibility in dynamic spectrum environments. Our model implements multi-task learning for concurrent primary user detection and MBMS signal classification, featuring adaptive thresholds that adjust to signal conditions. Develops a novel multi-task learning-based spectrum sensing framework for concurrent primary user detection and MBMS signal classification. Introduces adaptive thresholding mechanisms to improve detection robustness under varying SNR conditions. Achieves 99% classification accuracy at −10 dB SNR, significantly outperforming traditional methods. Demonstrates practical feasibility for real-time spectrum sensing in 5G-MBMS networks.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 3","pages":"706-716"},"PeriodicalIF":4.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Coding Mode Decision for Intra Prediction in VVC SCC 基于VVC SCC的帧内预测快速编码模式决策

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-29 DOI: 10.1109/TBC.2025.3541773

Dayong Wang;Weihong Liu;Zeyu Zhou;Xin Lu;Jinhua Liu;Hui Guo;Ce Zhu

{"title":"Fast Coding Mode Decision for Intra Prediction in VVC SCC","authors":"Dayong Wang;Weihong Liu;Zeyu Zhou;Xin Lu;Jinhua Liu;Hui Guo;Ce Zhu","doi":"10.1109/TBC.2025.3541773","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541773","url":null,"abstract":"Currently, screen content video applications are widely used in our daily lives. As the latest Screen Content Coding (SCC) standard, Versatile Video Coding (VVC) SCC employs a quad-tree plus nested multi-type tree (QTMT) coding structure and various screen content coding modes (CMs). This design enhances the coding efficiency of VVC SCC but also results in a highly complex coding process, which significantly hinders the broader adoption of screen content video technology. Consequently, improving the coding speed of VVC SCC is highly desirable. In this paper, we propose a fast CM and transform decision algorithm for Intra prediction in VVC SCC. Specifically, we initially use Convolutional Neural Networks (CNNs) to predict content types for all Coding Units (CUs). Subsequently, we predict candidate CMs for CUs based on the CM distributions of different content types. We then select the Sum of Absolute Transformed Difference (SATD) as a feature and use a naive Bayes classifier to skip unlikely Intra mode early. Finally, we terminate Block-based Differential Pulse-Code Modulation (BDPCM) early and then select the best transform type in Intra mode prediction to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed by an average of 39.28%, with the BDBR increasing by 0.80%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"506-516"},"PeriodicalIF":3.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

No-Reference Image Quality Assessment via Inter-Level Adaptive Knowledge Distillation 基于层次间自适应知识蒸馏的无参考图像质量评估

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-26 DOI: 10.1109/TBC.2025.3549985

Bo Hu;Wenzhi Chen;Jia Zheng;Leida Li;Wen Lu;Xinbo Gao

{"title":"No-Reference Image Quality Assessment via Inter-Level Adaptive Knowledge Distillation","authors":"Bo Hu;Wenzhi Chen;Jia Zheng;Leida Li;Wen Lu;Xinbo Gao","doi":"10.1109/TBC.2025.3549985","DOIUrl":"https://doi.org/10.1109/TBC.2025.3549985","url":null,"abstract":"Compared with no-reference image quality assessment (IQA), full-reference IQA often achieves higher consistency with human subjective perception due to the reference information for comparison. A natural idea is to design strategies that allow the latter to guide the former’s learning to achieve better performance. However, how to construct the reference information and how to transfer prior knowledge are two important issues we are going to face that have not been fully explored. To this end, a novel method called no-reference IQA via inter-level adaptive knowledge distillation (AKD-IQA) is proposed. The core of AKD-IQA lies in transferring image distribution difference information from the full-reference teacher model to the no-reference student model through inter-level AKD. First, the teacher model is constructed based on multi-level feature discrepancy extractor and cross-scale feature integrator. Then, it is trained on a large synthetic distortion dataset to establish a comprehensive difference prior distribution. Finally, the image re-distortion strategy and inter-level AKD are introduced into the student model for effective learning. Experimental results on six standard IQA datasets demonstrate that the AKD-IQA achieves state-of-the-art performance. In addition, cross-dataset experiments confirm the superiority of it in generalization ability.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"581-592"},"PeriodicalIF":3.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0