IEEE Transactions on Broadcasting最新文献

{"title":"Advancing No-Reference Point Cloud Quality Assessment via Leveraging Prior Knowledge From Ranking Learning","authors":"Jilong Wang;Yijie Chen;Yinyin Gong;Liang Lin","doi":"10.1109/TBC.2026.3696545","DOIUrl":"https://doi.org/10.1109/TBC.2026.3696545","url":null,"abstract":"Point clouds have emerged as a crucial representation for 3D visual content, driving innovations in immersive media applications and broadcasting. The development of no-reference point cloud quality assessment (PCQA) methods is crucial for automating quality monitoring and enhancing user experiences. However, the advancement of PCQA is hindered by the limited scale and diversity of existing databases, coupled with the insufficient exploitation of the intrinsic data information and relationships. To overcome these limitations, we first introduce the PCL-PCQA database, a newly constructed large-scale PCQA database comprising 1,599 subjectively evaluated distorted point cloud samples with diverse geometric and textural distortions, derived from 39 high-quality reference point clouds and labeled with reliable mean opinion scores (MOS) following the ITU-R BT.500 standard. Second, we propose a novel ranking-based no-reference PCQA model, Rank-PCQA, which leverages prior knowledge from inherent quality rankings embedded in distortion levels through a two-stage learning pipeline. This approach involves a pre-training stage with pairwise ranking loss and level-wise boundary regularization to learn hierarchical quality relationships, followed by fine-tuning for accurate MOS regression. Extensive experiments on the SJTU-PCQA, WPC, LS-PCQA, and our PCL-PCQA databases demonstrate that our Rank-PCQA achieves state-of-the-art or highly competitive performance against existing advanced methods, validating its effectiveness and strong generalization capability. The database and source code will be available at <uri>https://github.com/KunChun/Rank-PCQA</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"931-946"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902842","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":"Adaptive HTTP Video Streaming Over Multipath TCP: Measurement, Analysis, and Optimization","authors":"Jia Zhao;Jiangchuan Liu;Cong Zhang;Yong Cui;Yong Jiang;Wei Gong;Xiping Hu","doi":"10.1109/TBC.2026.3696565","DOIUrl":"https://doi.org/10.1109/TBC.2026.3696565","url":null,"abstract":"HTTP-based adaptive video streaming has become a key contributor to the Internet traffic. The streaming quality however is heavily affected by the throughput and stability of the underlying TCP. Being compatible and fair, multipath TCP (MPTCP) has recently been suggested as a promising enhancement to the conventional TCP, particularly considering that multi-home is becoming ubiquitous for modern networked devices. The opportunities and challenges of adaptive streaming over MPTCP however remain largely unclear to the research community. This paper presents a systematic study on adaptive streaming over MPTCP. We start from realworld experiments with Dynamic Adaptive Streaming over HTTP (DASH) and analysis on its performance over MPTCP. We show that DASH can greatly benefit from the improved aggregated throughput by MPTCP; yet the inter-path throughput difference and the intra-path throughput fluctuation have noticeable (negative) impact, too. Without a proper design of path selection and adaptation in MPTCP, they can easily confuse the adaptation logic of DASH, resulting in low bitrates or frequent rebuffering even if high-bandwidth paths are available. We present MPTCP+, an extended multipath TCP solution to offer high quality and smooth playback for adaptive HTTP streaming. MPTCP+ incorporates a path use decision algorithm that smartly disables/enables a path to minimize the inter-path difference, and a novel congestion control algorithm that smooths congestion window evolution with multiple paths. We have implemented MPTCP+ in the MPTCP Linux kernel, with minimum change on the server-side MPTCP module only. It is fully compatible with the existing MPTCP clients and requires no change on the upper-layer protocols, too. Our experiments suggest that MPTCP+ increases the quality of experience (QoE) of DASH by up to 50%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"990-1005"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903053","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":"UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment","authors":"Bingxu Xie;Fang Zhou;Jincan Wu;Yonghui Liu;Weiqing Li;Zhiyong Su","doi":"10.1109/TBC.2026.3666667","DOIUrl":"https://doi.org/10.1109/TBC.2026.3666667","url":null,"abstract":"While no-reference point cloud quality assessment (NR-PCQA) approaches have achieved significant progress over the past decade, their performance often degrades substantially when a distribution gap exists between the training (source domain) and testing (target domain) data. However, to date, limited attention has been paid to transferring NR-PCQA models across domains. To address this challenge, we propose the first unsupervised progressive domain adaptation (UPDA) framework for NR-PCQA, which introduces a two-stage coarse-to-fine alignment paradigm to address domain shifts. At the coarse-grained stage, a discrepancy-aware coarse-grained alignment method is designed to capture relative quality relationships between cross-domain samples through a novel quality-discrepancy-aware hybrid loss, circumventing the challenges of direct absolute feature alignment. At the fine-grained stage, a perception fusion fine-grained alignment approach with symmetric feature fusion is developed to identify domain-invariant features, while a conditional discriminator selectively enhances the transfer of quality-relevant features. Extensive experiments demonstrate that the proposed UPDA effectively enhances the performance of NR-PCQA methods in cross-domain scenarios, validating its practical applicability. The code is available at <uri>https://github.com/yokeno1/UPDA-main</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"920-930"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903176","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 Readers and Authors","authors":"","doi":"10.1109/TBC.2026.3726704","DOIUrl":"https://doi.org/10.1109/TBC.2026.3726704","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"C3-C4"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11678979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903238","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":"Low-Complexity MAP Detection and Decoding to Improve BER Performance of Satellite Broadcasting Systems With LDM","authors":"Yuki Koizumi;Masaaki Kojima;Masashi Kamei;Kazuhiko Fukawa","doi":"10.1109/TBC.2026.3705496","DOIUrl":"https://doi.org/10.1109/TBC.2026.3705496","url":null,"abstract":"This paper proposes low-complexity detection and decoding based on the maximum <inline-formula> <tex-math>$a$ </tex-math></inline-formula> <italic>posteriori</i> (MAP) criterion for satellite broadcasting systems that use layered division multiplexing (LDM). The first component of this scheme exploits pilot signals to compensate for nonlinear distortion caused by satellite transponders. Specifically, received data signals suffer from nonlinear distortion and are classified into several bit-dependent clusters, of which the centroids can be obtained from the corresponding received pilot signals. The distance between each centroid and a received data signal yields the log likelihood ratio (LLR) of each data bit with sufficient accuracy. Since this component can almost nullify the nonlinear distortion, it is expected to outperform conventional successive interference cancellation (SIC), which has been widely used with the LDM system. To further improve LDM performance, the second component of the proposed scheme performs iterative decoding that reduces the otherwise prohibitive computational complexity in the MAP decoding. To cope with the short-loop issue inherent in the iterative decoding for LDM, the decoding process and associated parameters are optimized on the basis of numerical analyses. Specifically, a new decoding scheme that uses the <inline-formula> <tex-math>$a$ </tex-math></inline-formula> <italic>posteriori</i> LLR as a criterion is derived from an analysis of the relationship between prior and extrinsic information in iterative decoding. This scheme can determine whether specific extrinsic information should be used for the iterative decoding or not. Numerical analyses also show how inter-layer interference dominance depends on the inter-layer power ratio and how the resulting LLR reliability affects the decoding performance in LDM. Computer simulations over nonlinear channels created by emulating actual satellite transponder characteristics demonstrate that the pilot-aided compensation (the first component) can successfully mitigate the nonlinear distortion, while the optimized iterative decoding (the second component) can further improve the bit error rate (BER).","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"812-825"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11585717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902759","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":"LV-BLiF: Harnessing Language-Vision Representation Learning for Blind Light Field Image Quality Assessment","authors":"Xin Liao;Xiongli Chai;Hangwei Chen;Weiyi Jing;Feng Shao;Qiuping Jiang","doi":"10.1109/TBC.2026.3668512","DOIUrl":"https://doi.org/10.1109/TBC.2026.3668512","url":null,"abstract":"Light Field Images (LFIs) capture both angular and spatial information, enabling immersive visual experiences for observers. However, during acquisition and processing, LFIs often suffer from various degradations, accompanied by intricate spatio-angular distortions. Consequently, objective LFI Quality Assessment (LFIQA) has garnered significant research interest. Existing blind LFIQA methods predominantly operate in a content-agnostic manner. They primarily model low-level distortions while neglecting the semantic context, a limitation that significantly diverges from the perception mechanisms of the Human Visual System (HVS). To bridge the gap between artifact analysis and scene understanding, we propose LV-BLiF, a novel dual-branch LFIQA network based on language-vision representation learning. Firstly, the textual prompts-assisted branch utilizes a pre-trained quality-aware Large Multimodal Model (LMM) to extract deep semantic representations, which are transformed into semantic context vectors sensitive to spatial-angular quality variations via pseudo-label guided learning strategy. Subsequently, semantic quality scores are generated based on this high-level information. Secondly, the visual cues-assisted branch employs disentangling convolutions to decompose light field features into spatial, angular, and epipolar plane image subspaces. These features are processed by Light Field Residual Blocks (LFRBs) and adaptively integrated via a Mixture-of-Experts-like (MoE-like) gating mechanism to predict visual quality scores. Finally, the semantic and visual scores are fused through a learnable adaptive gating mechanism to derive the overall LFI quality score. Extensive experiments on benchmark datasets, including NBU, SHU, and Win5-LID, demonstrate that LV-BLiF significantly outperforms state-of-the-art methods and exhibits high consistency with human perception. The proposed metric will be publicly available at <uri>https://github.com/shuaibilx/LV-BLiF</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"960-974"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902638","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":"Inter-Frame Reference Dependency Motivated Initial Quantization Parameter Selection for Screen Content Video Rate Control Using VVC","authors":"Yanchao Gong;Yinghua Li;Hong Guo;Kaifang Yang;Nam Ling","doi":"10.1109/TBC.2026.3668522","DOIUrl":"https://doi.org/10.1109/TBC.2026.3668522","url":null,"abstract":"Rate control (RC) plays a crucial role in video services, with the aim of improving video quality while meeting the channel bandwidth constraint. Initial quantization parameter selection (IQS) is one of the key techniques in RC, which significantly affects the performance of RC. The IQS recommended by the state-of-the-art VVC standard ignores the important influence of inter-frame reference dependency (IRD), resulting in low coding efficiency. To address this issue, an IRD motivated IQS method for screen content videos (SCVs) using VVC, referred to as IRD-IQS-SCV, is proposed in this paper. First, the theoretical analysis and experimental verification are conducted to prove that the IRD is a key factor influencing the IQS. Then, a novel bit allocation model considering the IRD is created. Furthermore, the bit allocation model is effectively solved through the interior point method and the designed SCV classification method, upon which the IRD-IQS-SCV is formulated. Compared with the IQS method recommended by VVC, the average BD-rate corresponding to the IRD-IQS-SCV is -7.13% for all test sequences, with BE savings of 2.59%, quality fluctuation degree reduction of 3.58%, and computational complexity savings of 4.78%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"1006-1023"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902686","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":"Deep Dynamic Point Cloud Attribute Compression Using Dual-Modal Motion Estimation and Spatio-Temporal Conditional Residual Coding","authors":"Zhiwei Guo;Yun Zhang;Linwei Zhu","doi":"10.1109/TBC.2026.3689340","DOIUrl":"https://doi.org/10.1109/TBC.2026.3689340","url":null,"abstract":"Dynamic point cloud is a sequence of point cloud, providing 3D geometry coordinates, attribute as well as temporal information. However, its massive data volume hinders the data processing, storage and network transmission for 3D applications. To address this problem, we propose an end-to-end Deep Dynamic Point Cloud Attribute Compression (DD-PCAC) to exploit the temporal redundancies in dynamic point cloud. First, we design a unified DD-PCAC framework that performs motion estimation, compensation, and residual encoding in a fully differentiable pipeline. Second, we propose a Dual-Modal Motion Estimation (DMME) module to explore temporal inter-frame redundancies of dynamic point cloud, which includes Dynamic Ball Matching (DBM), Pixel-domain Aggregation (PDA), and Deep Feature Aggregation (DFA) modules. Third, we propose a spatio-temporal conditional residual encoder-decoder to effectively model temporal dependencies of point cloud attribute and improve compression efficiency. Experimental results show that the proposed DD-PCAC achieves an average of 31.57% and 30.33% BD-rate gains compared with the state-of-the-art TSC-PCAC model and the traditional Geometry-based Point Cloud Compression (G-PCC), respectively. Moreover, the encoding and decoding complexities of the proposed DD-PCAC are much lower than the benchmark G-PCC, V-PCC and Sparse-PCAC. The source code and pretrained models are available at: <uri>https://github.com/SYSU-Video/DD-PCAC</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"877-889"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902701","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":"DRL-Optimized UAV-Relayed 6G Multicast/Broadcast for Over-the-Air IoT Firmware Updates","authors":"Fang Xu;Jie Wang;Libiao Jin;Yiyuan Xie;Fei Qi;Xuan Shi","doi":"10.1109/TBC.2026.3679989","DOIUrl":"https://doi.org/10.1109/TBC.2026.3679989","url":null,"abstract":"The evolution toward 6G networks introduces unprecedented opportunities for efficient multicast and broadcast services, particularly for Over-the-Air (OTA) firmware updates in massive IoT deployments. This paper proposes a DRL-optimized UAV-relayed 6G multicast/broadcast architecture that delivers large-scale OTA IoT updates while maintaining high spectral efficiency and stringent delay targets. We address the critical challenge of delivering OTA firmware updates to millions of IoT devices across diverse geographical regions by leveraging 6G broadcast/multicast capabilities through a hybrid satellite-UAV architecture. The proposed DRL-based solution employs a Deep Q-Network (DQN) with prioritized experience replay to dynamically optimize broadcast coverage areas, multicast group assignments, and transmission scheduling for OTA updates. Our framework incorporates action dimension reduction techniques to handle the massive scale of IoT devices while ensuring efficient spectrum utilization and minimizing update completion time. Experimental results show that compared with the unicast method, the DRL-enabled broadcast system can significantly shorten the OTA update completion time and maintain higher update reliability in challenging environmental conditions. The proposed architecture supports scalable firmware distribution to over 100,000 IoT devices simultaneously, making it suitable for smart city and industrial IoT applications requiring frequent security patches and feature updates.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"799-811"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902844","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":"Adaptive Picture Mirroring for Video Coding Efficiency Enhancement","authors":"Mateusz Lorkiewicz;Dawid Mieloch;Jakub Kit;Jakub Stankowski;Adrian Dziembowski;Damian Karwowski;Sławomir Maćkowiak","doi":"10.1109/TBC.2026.3713269","DOIUrl":"https://doi.org/10.1109/TBC.2026.3713269","url":null,"abstract":"The exponential growth of global video traffic imposes severe constraints on available transmission bandwidth, necessitating continuous improvements in compression efficiency. While modern hybrid video codecs optimize bit allocation locally, they often fail to exploit the global characteristics, such as dominant edge directionality. To address this limitation, this paper proposes a bandwidth-efficient, lightweight pre-processing framework that adaptively mirrors encoded video frames to align their structural features with the encoder prediction tools. Unlike previous approaches that rely on computationally expensive brute-force search or complex neural networks, the proposed method uses a fast, heuristic decision algorithm based on edge-direction analysis. Extensive evaluations were conducted using the state of-the-art Enhanced Compression Model (ECM) and the industry-standard HEVC, covering both All-Intra and Random Access (RA) configurations to simulate realistic broadcasting scenarios. The results confirm that the framework yields noticeable coding gains, particularly for content with dominant diagonal geometric structures, achieving average BD-rate savings of <inline-formula> <tex-math>$mathbf {-0.23%}$ </tex-math></inline-formula> (ECM All-Intra) and <inline-formula> <tex-math>$mathbf {-0.11%}$ </tex-math></inline-formula> (ECM Random Access), with specific high-definition sequences (Class B) reaching gains up to <inline-formula> <tex-math>$mathbf {-0.58%}$ </tex-math></inline-formula>. Cross-codec validation using HEVC confirms the universality of the proposal, yielding an average savings of <inline-formula> <tex-math>$mathbf {-0.30%}$ </tex-math></inline-formula>. Crucially, complexity analysis reveals that the method introduces negligible computational overhead (approximately 30 ms of pre-analysis per 4K frame, making it a highly viable, lightweight extension for video distribution pipelines where reference encoder runtimes dominate.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"851-862"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11625970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902936","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}