IEEE Transactions on Broadcasting最新文献_第2页

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

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

A Low-Sampling-Rate Digital Predistortion Method Based on Inverse Filter Signal Recovery for Wideband Power Amplifiers 基于反滤波信号恢复的宽带功率放大器低采样率数字预失真方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-25 DOI: 10.1109/TBC.2025.3549995

Xiaofang Wu;Jiawen Yan;Dehuang Zhang;Jianyang Zhou

{"title":"A Low-Sampling-Rate Digital Predistortion Method Based on Inverse Filter Signal Recovery for Wideband Power Amplifiers","authors":"Xiaofang Wu;Jiawen Yan;Dehuang Zhang;Jianyang Zhou","doi":"10.1109/TBC.2025.3549995","DOIUrl":"https://doi.org/10.1109/TBC.2025.3549995","url":null,"abstract":"To address the high cost associated with using high-speed and large-acquisition-bandwidth analog-to-digital-converters (ADCs) in the feedback path, a new low-sampling-rate digital predistortion (DPD) method is proposed in this paper. To model the analog bandpass filter (BPF) in the feedback path, a training method for digital finite impulse response (FIR) filter coefficients in a practical band-limited DPD system is proposed, and a filter matrix is constructed in different forms in the case of continuous signal and cyclic signal inputs. The filter matrix provides an extra degree of band-limited power amplifier (PA) model accuracy and robustness. Then, an inverse filter signal recovery (IFSR) method is proposed to recover the full-band output signal of the PA, which can be used to train the predistorter using conventional DPD techniques. Simulation results validates the effectiveness of the IFSR method, demonstrating that the IFSR-DPD method can reduce the ADC sampling rate to 1/10 or less compared to full-rate sampling methods, and decrease the ADC acquisition bandwidth to about 0.3 times that of the original input signal bandwidth. The linearization performance of the IFSR-DPD method is also evaluated on an instrument-based test platform. When the passband and transition band characteristics of the BPF are unsatisfactory, the proposed low-sampling rate DPD method improves the adjacent channel power ratio (ACPR) by 18.67 dB and the error vector magnitude (EVM) by 1.214%, compared to the scenario without DPD.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"653-665"},"PeriodicalIF":3.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243906","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

Parameter Estimation for Adaptive Impulsive Noise Suppression: A Deep Learning-Based Memoryless Nonlinearity Approach 自适应脉冲噪声抑制参数估计：基于深度学习的无记忆非线性方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-24 DOI: 10.1109/TBC.2025.3550016

Zhu Xiao;Yiqiu Zhang;Tong Li;Jing Bai;Siwang Zhou;Yonghu Zhang

{"title":"Parameter Estimation for Adaptive Impulsive Noise Suppression: A Deep Learning-Based Memoryless Nonlinearity Approach","authors":"Zhu Xiao;Yiqiu Zhang;Tong Li;Jing Bai;Siwang Zhou;Yonghu Zhang","doi":"10.1109/TBC.2025.3550016","DOIUrl":"https://doi.org/10.1109/TBC.2025.3550016","url":null,"abstract":"In the OFDM-based digital terrestrial broadcasting systems, impulsive noise is a significant factor affecting communication quality. A prominent method to suppress impulsive noise is to incorporate a memoryless nonlinearity at the receiver front-end of the OFDM demodulator, in which parameter estimation of memoryless nonlinearity directly impact the effectiveness of impulsive noise suppression. In this paper, we proposes a deep learning-based memoryless nonlinearity approach for impulsive noise suppression. The proposed method can adaptively estimate the parameters of the memoryless nonlinearity in dynamic impulsive noise environments and achieve totically-optimal parameter estimation. To specific, we design a High-Amplitude Priority Downsampling method to extract the key amplitude characteristics from the input signal, which effectively resolves the issue of extracting amplitude features of impulsive noise. Besides, to address the issue of performance degradation due to insufficient training samples, we propose a novel training method that integrates progressive fine-tuning to complete the training only using few samples. Furthermore, we conduct experiments on signal-to-noise ratio (SNR) and bit error rate (BER) of the signal after impulsive noise suppression. The results validate that the parameters estimated by the proposed method can approximate the theoretical optimal values and the proposed method can effectively suppress impulsive noise and outperform the traditional methods in terms of SNR and BER.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"641-652"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243907","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

Terahertz Hybrid Precoding With Low-Resolution PSs Under Frequency Selective Channel: A Partial Decoupling Method 频率选择信道下低分辨率ps的太赫兹混合预编码：一种部分解耦方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-21 DOI: 10.1109/TBC.2025.3550020

Yang Wang;Chuang Yang;Mugen Peng

{"title":"Terahertz Hybrid Precoding With Low-Resolution PSs Under Frequency Selective Channel: A Partial Decoupling Method","authors":"Yang Wang;Chuang Yang;Mugen Peng","doi":"10.1109/TBC.2025.3550020","DOIUrl":"https://doi.org/10.1109/TBC.2025.3550020","url":null,"abstract":"Terahertz (THz) communication is considered as one of the most critical technologies for 6G broadcasting communications because of its abundant bandwidth. To compensate for the high propagation of THz, analog/digital hybrid precoding for THz massive multiple input multiple output (MIMO) is proposed to focus signals and extend the broadcasting communication range. Notably, considering hardware cost and power consumption, infinite and high-resolution phase shifters (PSs) are difficult to implement in THz massive MIMO, and low-resolution PSs are typically adopted in practice. However, low-resolution PSs cause severe performance degradation, which also poses challenges for the design of analog precoders for multi-carrier systems. Moreover, THz communication with broadband suffers severe frequency selective fading, further increasing the analog precoder design difficulty. Motivated by the above factors, in this paper, we propose a new heuristic algorithm under a fully connected (FC) structure and partially-connected (PC) architecture, which firstly decouples partially the digital precoder and the analog precoder and then optimizes alternately. To further improve the performance, we extend our partial decoupling method to dynamic subarrays in which each RF chain is connected to an antenna that does not duplicate. The numerical results demonstrate that our proposed THz hybrid precoding with low-resolution PSs achieves better performance to the comparisons for both FC structure and PC structure.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"453-466"},"PeriodicalIF":3.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243589","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

Using Deep Reinforcement Learning (DRL) to Optimize Quality in 360-Degree Video Tile Management 使用深度强化学习（DRL）优化360度视频贴图管理的质量

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-11 DOI: 10.1109/TBC.2025.3541860

Chunguang Li;Dayoung Lee;Minseok Song

{"title":"Using Deep Reinforcement Learning (DRL) to Optimize Quality in 360-Degree Video Tile Management","authors":"Chunguang Li;Dayoung Lee;Minseok Song","doi":"10.1109/TBC.2025.3541860","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541860","url":null,"abstract":"360-degree videos inherently require significant storage space because each segment consists of many tiles, each of which is further transcoded and stored in multiple versions. It is thus impractical to store all transcoded versions, which makes it essential to make effective use of limited storage space. However, the inefficiency of existing heuristic-based management schemes arises from the challenge of incorporating various factors, such as variable bandwidth requirements influenced by network conditions, tile access distribution, and video quality dependent on content. To address this, we propose a new storage space management scheme, which combines the dueling deep Q-network (DQN) algorithm based on the field-of-view (FoV) distribution and the greedy algorithm that considers the overall video popularity. We first model an environment in which the agent can determine the versions for each tile to achieve the best video quality under various storage limit conditions. The dueling DQN environment comprises 1) an action space determining version combinations for each tile within specified storage limits, 2) an observation space enabling the agent to learn variable bandwidths and tile access distributions, and 3) a reward model deriving the expected video quality for different actions. Building upon the dueling DQN model correlating storage limits with expected video quality, we present a greedy algorithm that selects versions among multiple videos within storage limits for the purpose of maximizing popularity-weighted video quality. Extensive simulations evaluated the proposed scheme under various storage limits, bandwidth changes, and FoV distributions, demonstrating an improvement in overall popularity-weighted video quality ranging from 0.49% to 37.77% (with an average improvement of 13.96%) compared to existing benchmark schemes.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"555-569"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243592","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

FTBM: A Fault-Tolerant BIER Multicast for MBMS in 5G/B5G Dynamic Edge Networks FTBM: 5G/B5G动态边缘网络中MBMS的容错BIER组播

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-07 DOI: 10.1109/TBC.2025.3541889

Honglin Fang;Peng Yu;Xinxiu Liu;Ying Wang;Wenjing Li;Xuesong Qiu;Zhaowei Qu

{"title":"FTBM: A Fault-Tolerant BIER Multicast for MBMS in 5G/B5G Dynamic Edge Networks","authors":"Honglin Fang;Peng Yu;Xinxiu Liu;Ying Wang;Wenjing Li;Xuesong Qiu;Zhaowei Qu","doi":"10.1109/TBC.2025.3541889","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541889","url":null,"abstract":"The evolution of 5G and Beyond 5G (B5G) networks has intensified the demand for efficient Multimedia Broadcast Multicast Services (MBMS), particularly in dynamic edge environments. The frequent alterations in network topology and multicast group configurations in these environments present substantial scalability challenges for traditional IP MultiCast (IPMC) mechanisms. Bit Index Explicit Replication (BIER) offers a stateless IPMC alternative that mitigates the limitations of traditional IPMC mechanisms. However, it still encounters fault tolerance issues in dynamic edge networks, where link faults occur frequently. This paper propose a Fault-Tolerant BIER Multicast (FTBM) mechanism specifically designed for MBMS in dynamic edge networks. FTBM optimizes BIER multicast paths by employing Multi-Agent Deep Reinforcement Learning (MADRL) to minimize transmission delays while addressing constraints such as random link faults, limited queue capacity, and forwarding restrictions. Extensive simulations demonstrate that FTBM significantly enhances multicast performance under varying traffic loads and dense fault conditions, leading to improved transmission efficiency and network load balancing. This work provides a resilient and scalable solution for next-generation MBMS in dynamic network environments.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"411-425"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243676","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

IEEE Transactions on Broadcasting Publication Information IEEE广播出版信息汇刊

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-05 DOI: 10.1109/TBC.2025.3542624

引用次数: 0

IEEE Transactions on Broadcasting Information for Authors IEEE作者广播信息汇刊

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-05 DOI: 10.1109/TBC.2025.3542626

引用次数: 0