IEEE Transactions on Broadcasting最新文献

{"title":"Rate-Splitting Multiple Access for Overloaded Multi-Group Multicast: A First Experimental Study","authors":"Xinze Lyu;Sundar Aditya;Bruno Clerckx","doi":"10.1109/TBC.2024.3475743","DOIUrl":"https://doi.org/10.1109/TBC.2024.3475743","url":null,"abstract":"Multi-group multicast (MGM) is an increasingly important form of multi-user wireless communications with several potential applications, such as video streaming, federated learning, safety-critical vehicular communications, etc. Rate-Splitting Multiple Access (RSMA) is a powerful interference management technique that can, in principle, achieve higher data rates and greater fairness for all types of multi-user wireless communications, including MGM. This paper presents the first-ever experimental evaluation of RSMA-based MGM, as well as the first-ever three-way comparison of RSMA-based, Space Division Multiple Access (SDMA)-based and Non-Orthogonal Multiple Access (NOMA)-based MGM. Using a measurement setup involving a two-antenna transmitter and two groups of two single-antenna users per group, we consider the problem of realizing throughput (max-min) fairness across groups for each of three multiple access schemes, over nine experimental cases in a line-of-sight environment capturing varying levels of pathloss difference and channel correlation across the groups. Over these cases, we observe that RSMA-based MGM achieves fairness at a higher throughput for each group than SDMA- and NOMA-based MGM. These findings validate RSMA-based MGM’s promised gains from the theoretical literature.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"30-41"},"PeriodicalIF":3.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553030","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}

{"title":"A Fast CU Partition Algorithm for AVS3 Based on Adaptive Tree Search and Pruning Optimization","authors":"Jihang Yin;Honggang Qi;Liang Zhong;Zhiyuan Zhao;Qiang Wang;Jingran Wu;Xianguo Zhang","doi":"10.1109/TBC.2024.3465838","DOIUrl":"https://doi.org/10.1109/TBC.2024.3465838","url":null,"abstract":"In the third generation of the Audio Video Coding Standard (AVS3), the size of Coding Tree Units (CTUs) has been expanded to four times larger than the previous generation, and more Coding Unit (CU) partition modes have been introduced, enhancing adaptability and efficiency in video encoding. CU partition in AVS3 not only brings improvements in encoding performance but also significantly increases the computational complexity, posing substantial challenges to real-time encoding. We propose a fast algorithm for CU partition, which features adaptive tree search and pruning optimization. Firstly, it adjusts the tree search order based on neighbor CU and lookahead information. Specifically, the analysis order of sub-blocks and parent blocks is adaptively adjusted: the potential optimal partition is prioritized, the non-optimal partitions are deferred, and an optimized order of first-full-then-sub or first-sub-then-full is selected. Secondly, the pruning optimization algorithm utilizes analyzed information to skip non-optimal partitions to reduce computational complexity. Due to the adjusted tree search order and the prioritization of potential optimal partitions, more analyzed information becomes available when evaluating non-optimal partitions, thereby improving the recall and precision rates of non-optimal partitions detection, saving more time, and introducing negligible loss in coding performance. The proposed algorithm has been implemented in the open-source encoder uavs3e. Experimental results indicate that under the three encoding configurations of AI, LD B, and RA, the algorithm achieves significant time saving of 51.41%, 40.57%, and 40.57%, with BDBR increases of 0.64%, 1.61%, and 1.04%, respectively. These results outperform the state-of-the-art fast CU partition algorithms.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"125-141"},"PeriodicalIF":3.2,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553217","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}

{"title":"From Pixels to Rich-Nodes: A Cognition-Inspired Framework for Blind Image Quality Assessment","authors":"Tian He;Lin Shi;Wenjia Xu;Yu Wang;Weijie Qiu;Houbang Guo;Zhuqing Jiang","doi":"10.1109/TBC.2024.3464418","DOIUrl":"https://doi.org/10.1109/TBC.2024.3464418","url":null,"abstract":"Blind image quality assessment (BIQA) is a subjective perception-driven task, which necessitates assessment results consistent with human cognition. The human cognitive system inherently involves both separation and integration mechanisms. Recent works have witnessed the success of deep learning methods in separating distortion features. Nonetheless, traditional deep-learning-based BIQA methods predominantly depend on fixed topology to mimic the information integration in the brain, which gives rise to scale sensitivity and low flexibility. To handle this challenge, we delve into the dynamic interactions among neurons and propose a cognition-inspired BIQA model. Drawing insights from the rich club structure in network neuroscience, a graph-inspired feature integrator is devised to reconstruct the network topology. Specifically, we argue that the activity of individual neurons (pixels) tends to exhibit a random fluctuation with ambiguous meaning, while clear and coherent cognition arises from neurons with high connectivity (rich-nodes). Therefore, a self-attention mechanism is employed to establish strong semantic associations between pixels and rich-nodes. Subsequently, we design intra- and inter-layer graph structures to promote the feature interaction across spatial and scale dimensions. Such dynamic circuits endow the BIQA method with efficient, flexible, and robust information processing capabilities, so as to achieve more human-subjective assessment results. Moreover, since the limited samples in existing IQA datasets are prone to model overfitting, we devise two prior hypotheses: frequency prior and ranking prior. The former stepwise augments high-frequency components that reflect the distortion degree during the multilevel feature extraction, while the latter seeks to motivate the model’s in-depth comprehension of differences in sample quality. Extensive experiments on five publicly datasets reveal that the proposed algorithm achieves competitive results.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"229-239"},"PeriodicalIF":3.2,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553171","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}

{"title":"JND-LIC: Learned Image Compression via Just Noticeable Difference for Human Visual Perception","authors":"Zhaoqing Pan;Guoyu Zhang;Bo Peng;Jianjun Lei;Haoran Xie;Fu Lee Wang;Nam Ling","doi":"10.1109/TBC.2024.3464413","DOIUrl":"https://doi.org/10.1109/TBC.2024.3464413","url":null,"abstract":"Existing human visual perception-oriented image compression methods well maintain the perceptual quality of compressed images, but they may introduce fake details into the compressed images, and cannot dynamically improve the perceptual rate-distortion performance at the pixel level. To address these issues, a just noticeable difference (JND)-based learned image compression (JND-LIC) method is proposed for human visual perception in this paper, in which a weight-shared model is used to extract image features and JND features, and the learned JND features are utilized as perceptual prior knowledge to assist the image coding process. In order to generate a highly compact image feature representation, a JND-based feature transform module is proposed to model the pixel-to-pixel masking correlation between the image features and the JND features. Furthermore, inspired by eye movement research that the human visual system perceives image degradation unevenly, a JND-guided quantization mechanism is proposed for the entropy coding, which adjusts the quantization step of each pixel to further eliminate perceptual redundancies. Extensive experimental results show that our proposed JND-LIC significantly improves the perceptual quality of compressed images with fewer coding bits compared to state-of-the-art learned image compression methods. Additionally, the proposed method can be flexibly integrated with various advanced learned image compression methods, and has robust generalization capabilities to improve the efficiency of perceptual coding.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"217-228"},"PeriodicalIF":3.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553372","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}

{"title":"TSC-PCAC: Voxel Transformer and Sparse Convolution-Based Point Cloud Attribute Compression for 3D Broadcasting","authors":"Zixi Guo;Yun Zhang;Linwei Zhu;Hanli Wang;Gangyi Jiang","doi":"10.1109/TBC.2024.3464417","DOIUrl":"https://doi.org/10.1109/TBC.2024.3464417","url":null,"abstract":"Point cloud has been the mainstream representation for advanced 3D applications, such as virtual reality and augmented reality. However, the massive data amounts of point clouds is one of the most challenging issues for transmission and storage. In this paper, we propose an end-to-end voxel Transformer and Sparse Convolution based Point Cloud Attribute Compression (TSC-PCAC) for 3D broadcasting. Firstly, we present a framework of the TSC-PCAC, which includes Transformer and Sparse Convolutional Module (TSCM) based variational autoencoder and channel context module. Secondly, we propose a two-stage TSCM, where the first stage focuses on modeling local dependencies and feature representations of the point clouds, and the second stage captures global features through spatial and channel pooling encompassing larger receptive fields. This module effectively extracts global and local inter-point relevance to reduce informational redundancy. Thirdly, we design a TSCM based channel context module to exploit inter-channel correlations, which improves the predicted probability distribution of quantized latent representations and thus reduces the bitrate. Experimental results indicate that the proposed TSC-PCAC method achieves an average of 38.53%, 21.30%, and 11.19% bitrate reductions on datasets 8iVFB, Owlii, 8iVSLF, Volograms, and MVUB compared to the Sparse-PCAC, NF-PCAC, and G-PCC v23 methods, respectively. The encoding/decoding time costs are reduced 97.68%/98.78% on average compared to the Sparse-PCAC. The source code and the trained TSC-PCAC models are available at <uri>https://github.com/igizuxo/TSC-PCAC</uri>.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"154-166"},"PeriodicalIF":3.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553130","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}

{"title":"Enhancing Channel Estimation in Terrestrial Broadcast Communications Using Machine Learning","authors":"Iñigo Bilbao;Eneko Iradier;Jon Montalban;Pablo Angueira;Sung-Ik Park","doi":"10.1109/TBC.2024.3417228","DOIUrl":"https://doi.org/10.1109/TBC.2024.3417228","url":null,"abstract":"Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged as viable alternatives to conventional Physical Layer (PHY) signal processing methods. Specifically, in any wireless point-to-multipoint communication, accurate channel estimation plays a pivotal role in exploiting spectrum efficiency with functionalities such as higher-order modulation or full-duplex communication. This research paper proposes leveraging ML solutions, including Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs), to enhance channel estimation within broadcast environments. Each architecture is instantiated using distinct procedures, focusing on two fundamental approaches: channel estimation denoising and ML-assisted pilot interpolation. Rigorous evaluations are conducted across diverse configurations and conditions, spanning rural areas and co-channel interference scenarios. The results demonstrate that MLP and CNN architectures consistently outperform classical methods, yielding 10 and 20 dB performance improvements, respectively. These results underscore the efficacy of ML-driven approaches in advancing channel estimation capabilities for broadcast communication systems.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1181-1191"},"PeriodicalIF":3.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810675","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}

{"title":"IEEE Transactions on Broadcasting Information for Authors","authors":"","doi":"10.1109/TBC.2024.3453631","DOIUrl":"https://doi.org/10.1109/TBC.2024.3453631","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"C3-C4"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235706","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}

{"title":"Next-Gen Satellite System: Integrative Non-Orthogonal Broadcast and Unicast Services Based on Innovative Frequency Reuse Patterns","authors":"Shuai Han;Zhiqiang Li;Weixiao Meng;Cheng Li","doi":"10.1109/TBC.2024.3434731","DOIUrl":"10.1109/TBC.2024.3434731","url":null,"abstract":"The multibeam satellite system is crucial for providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within a limited spectrum of resources presents formidable challenges. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using rate-splitting multiple access (RSMA), which leverages non-orthogonal transmission and precoding strategies. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose two novel distributed frequency reuse (DFR) and centralized frequency reuse (CFR) strategies. Taking satellite beam gain characteristics and interference tolerance threshold into consideration, we further propose another two expansions of DFR and CFR strategies with innovative inner and outer divisions. For the NOBU joint transmission based on four novel frequency reuse patterns, we maximize the weighted sum rate (WSR). Subsequently, we introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problems into tractable convex counterparts. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1153-1166"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263748","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}

{"title":"IEEE Transactions on Broadcasting Publication Information","authors":"","doi":"10.1109/TBC.2024.3453629","DOIUrl":"https://doi.org/10.1109/TBC.2024.3453629","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"C2-C2"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235775","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}

{"title":"IEEE Transactions on Broadcasting Information for Authors","authors":"","doi":"10.1109/TBC.2024.3453611","DOIUrl":"https://doi.org/10.1109/TBC.2024.3453611","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"C3-C4"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235712","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}