IEEE Transactions on Broadcasting最新文献_第4页

Bidirectional Feature-Aligned Motion Transformation for Efficient Dynamic Point Cloud Compression 面向高效动态点云压缩的双向特征对齐运动变换

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-09 DOI: 10.1109/TBC.2026.3668610

Xuan Deng;Xingtao Wang;Xiandong Meng;Longguang Wang;Tiange Zhang;Debin Zhao;Xiaopeng Fan

{"title":"Bidirectional Feature-Aligned Motion Transformation for Efficient Dynamic Point Cloud Compression","authors":"Xuan Deng;Xingtao Wang;Xiandong Meng;Longguang Wang;Tiange Zhang;Debin Zhao;Xiaopeng Fan","doi":"10.1109/TBC.2026.3668610","DOIUrl":"https://doi.org/10.1109/TBC.2026.3668610","url":null,"abstract":"Efficient dynamic point cloud compression (DPCC) critically depends on accurate motion estimation and compensation. However, the inherently irregular structure and substantial local variations of point clouds make this task highly challenging. Existing approaches typically rely on explicit motion estimation, whose encoded motion vectors often fail to capture complex dynamics and inadequately exploit temporal correlations. To address these limitations, we propose a <bold>Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space. Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations, which serve as predictive context in a conditional coding pipeline, forming a unified “Motion + Conditional” representation. Built upon this bidirectional feature alignment, we introduce a Cross-Transformer Refinement module (CTR) at the decoder side to adaptively refine locally aligned features. By modeling cross-frame dependencies with vector attention, CRT enhances local consistency and restores fine-grained spatial details that are often lost during motion alignment. Moreover, we design a <bold>Random Access (RA) reference strategy that treats the bidirectionally aligned features as conditional context, enabling frame-level parallel compression and eliminating the sequential encoding. Extensive experiments demonstrate that Bi-FMT surpasses <bold>D-DPCC and <bold>AdaDPCC in both compression efficiency and runtime, achieving BD-Rate reductions of <bold>20% (D1) and <bold>9.4% (D1), respectively. Our code is available at <uri>https://github.com/dovedx/FMT_DPCC</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"520-532"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433388","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

Guest Editorial Special Issue on Brazilian DTV+ (TV 3.0): Transforming Broadcasting for the Future 特邀评论：巴西数字电视+ (TV 3.0)：面向未来的广播转型

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-06-15 DOI: 10.1109/TBC.2026.3696846

Cristiano Akamine;Yiyan Wu;Liang Zhang;Sung-Ik Park;Débora C. Muchaluat-Saade;Marcelo F. Moreno;Jeongchang Kim;Sungjun Ahn;Jon Montalban;Rafael Sotelo;Mylene Farias;Guido Lemos;Yin Xu;Luiz Fausto

引用次数: 0

A Highly Efficient Zero-Shot Image Denoiser 一种高效的零镜头图像去噪方法

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-27 DOI: 10.1109/TBC.2026.3691583

Ke Gu;Hongyan Liu;Yubin Gao;Kunpeng Mai;Kai Chen;Shu Jin;Guangtao Zhai

{"title":"A Highly Efficient Zero-Shot Image Denoiser","authors":"Ke Gu;Hongyan Liu;Yubin Gao;Kunpeng Mai;Kai Chen;Shu Jin;Guangtao Zhai","doi":"10.1109/TBC.2026.3691583","DOIUrl":"https://doi.org/10.1109/TBC.2026.3691583","url":null,"abstract":"Currently, there is an increasingly high demand for high-quality images across various streaming media applications, but visual signals are impaired by composite noise. The existing dataset-based image denoisers, requiring a huge number of clean-noisy image pairs from diverse scenes to well train a large-size deep network, often perform unwell in the real-world noisy image restoration due to the complexity and variability of scenes, limited computing resources and the difficulty in obtaining clean images. So this paper tries to propose a dataset-free highly efficient zero-shot image denoiser with a lightweight CNN, called HEZID, to achieve the effective real-time real-world noisy image restoration. First, for the problem of the BSN strategy that discards the center pixel to avoid identity mapping for image denoising, we propose a new solution by devising a dual-mode pixel-realignment strategy, which sequentially develops a ‘rough’ pixel-realignment method for smooth regions and a ‘fine’ pixel-realignment method for edge regions, therefore fully exploiting the center pixel and preventing identity mapping. Second, we develop a super-lightweight CNN-based denoiser based on the strip convolution, which is motivated by the high sensitivity of human eyes to horizontal and vertical stimuli for visual inputs, followed by introducing the residual learning and separable convolutions to severally facilitate feature propagation and reduce the computational burden. Experiments on multiple real-world noisy image datasets confirmed that our HEZID outperformed a dozen state-of-the-art peers in terms of restoration quality and running efficiency.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"505-519"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433396","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

NetXRate: Network-Assisted Rate Recommendation for XR Service Optimization NetXRate：针对XR业务优化的网络辅助费率建议

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-15 DOI: 10.1109/TBC.2026.3689326

Alejandro Lopez Garcia;Amr A. AbdelNabi;Miguel Catalan-Cid;Mario Montagud;Chathura Sarathchandra;Daniel Camps-Mur

{"title":"NetXRate: Network-Assisted Rate Recommendation for XR Service Optimization","authors":"Alejandro Lopez Garcia;Amr A. AbdelNabi;Miguel Catalan-Cid;Mario Montagud;Chathura Sarathchandra;Daniel Camps-Mur","doi":"10.1109/TBC.2026.3689326","DOIUrl":"https://doi.org/10.1109/TBC.2026.3689326","url":null,"abstract":"eXtended Reality (XR) Applications are characterized by stringent Quality of Service (QoS) demands, including low latency, high reliability, and sustained high data rates. These requirements are particularly challenging to satisfy in dynamic 5G network environments, where conventional Over-The-Top (OTT) rate control strategies—such as adaptive bitrate streaming and packet pacing—operate without visibility into network conditions. While recent efforts like CAMARA APIs aim to expose QoS capabilities, they primarily offer coarse-grained, reactive control mechanisms that do not address user-specific service degradation. This work introduces <italic>NetXRate, an O-RAN-compliant Network as a Service (NaaS) framework designed to enable user-centric rate adaptation for XR traffic. <italic>NetXRate integrates 3GPP Network Data Analytics Function (NWDAF) insights with a fairness-aware xApp deployed within the O-RAN Radio Intelligent Controller (RIC), enabling rate recommendations from the network to the XR application. Extensive simulation-based evaluations demonstrate that, compared to OTT adaptation methods, <italic>NetXRate achieves up to <bold>96% outage reduction when XR sessions are not prioritized and up to <bold>85% outage reduction when XR traffic is prioritized. Our results also demonstrate that by making more efficient use of spectrum, introducing <italic>NetXRate also enhances the performance of eMBB services in cells where both XR users and eMBB users coexist.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"477-494"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433387","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

STLTP Redundancy Architecture for MIMO Terrestrial Television Transmission: Implementation and Performance Analysis of Brazil's TV 3.0 (DTV+) Pilot MIMO地面电视传输的STLTP冗余架构：巴西TV 3.0 （DTV+）试点的实现和性能分析

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-14 DOI: 10.1109/TBC.2026.3689323

Boris Kauffmann;Luciano Neves dos Santos Junior;Cesar Augusto Diez Alves;Gunnar Bedicks;Cristiano Akamine

{"title":"STLTP Redundancy Architecture for MIMO Terrestrial Television Transmission: Implementation and Performance Analysis of Brazil's TV 3.0 (DTV+) Pilot","authors":"Boris Kauffmann;Luciano Neves dos Santos Junior;Cesar Augusto Diez Alves;Gunnar Bedicks;Cristiano Akamine","doi":"10.1109/TBC.2026.3689323","DOIUrl":"https://doi.org/10.1109/TBC.2026.3689323","url":null,"abstract":"This paper presents the design, implementation, and performance analysis of a cloud-originated Studio to Transmitter Link Transport Protocol (STLTP) redundancy architecture deployed for the Next-Generation Brazilian Digital Television System (TV 3.0) DTV+ pilot station in S<inline-formula> <tex-math>$tilde {text {a}}$ </tex-math></inline-formula>o Paulo, Brazil. The system represents the first cloud-based STLTP architecture for operational Multiple-Input Multiple-Output (MIMO) terrestrial television transmission, using a fully cloud-based infrastructure hosted on commercial cloud services. The architecture implements an active-active redundancy scheme using parallel STLTP streams over the public internet, protected by SMPTE 2022-1 Forward Error Correction (FEC) and Automatic Repeat Request (ARQ) protocols. The implementation supports one Ultra High Definition (UHD) (4K) and three High Definition (HD) (1080p) services at an aggregate bitrate of 32 Mbps, following Brazil’s DTV+ specifications based on the Advanced Television Systems Committee (ATSC) 3.0 physical layer with country-specific modifications. During a six-day measurement campaign, the system achieved zero packet loss at the exciter level, 100% service availability, and round-trip times of 1-3 ms between the cloud and the transmission site. Controlled network impairment experiments using Gilbert-Elliott burst loss models demonstrated complete ARQ recovery for burst durations up to 66 ms and identified a critical threshold where unrecoverable packet losses increase sharply when burst durations exceed approximately 525 ms under simultaneous dual-path degradation with a 300 ms buffer configuration. The results demonstrate that internet-based Studio-to-Transmitter Link (STL) delivery with appropriate redundancy can achieve broadcast-grade reliability, offering significant advantages over traditional dedicated links.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"676-686"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433392","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

Fusing Quaternion Rotations and Star Operations: A TLSNet-Based High-Precision 3-D Indoor Visible Light Positioning System 融合四元数旋转和星形运算：基于tlsnet的高精度三维室内可见光定位系统

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-02 DOI: 10.1109/TBC.2026.3664184

Tianyi Cao;Lei Jing;Shaorui Sang

{"title":"Fusing Quaternion Rotations and Star Operations: A TLSNet-Based High-Precision 3-D Indoor Visible Light Positioning System","authors":"Tianyi Cao;Lei Jing;Shaorui Sang","doi":"10.1109/TBC.2026.3664184","DOIUrl":"https://doi.org/10.1109/TBC.2026.3664184","url":null,"abstract":"This paper presents a deep-learning-enhanced visible light positioning (VLP) system for accurate 3-D localization under arbitrary receiver orientations. In a 3-D VLP system with a single LED, multiple photodiodes, and an IMU, the commonly used Euler-angle representation is replaced with sign-consistent unit quaternions as inputs to deep-learning models. By further enforcing explicit constraints to achieve a one-to-one, order-invariant mapping, this reparameterization resolves ambiguities and rotation-order dependency, providing a more structured orientation input and improving the achievable performance of the learning-based pipeline under a fixed system configuration. Based on these processed inputs, a compact neural architecture, termed twinkle little star net (TLSNet), is developed to explicitly exploit multiplicative and high-order feature interactions through a structured three-stage design consisting of feature supply, feature resifting, and feature recombination. This architecture enhances nonlinear representational capacity while maintaining parameter efficiency and stable optimization behavior. Experimental results demonstrate that the proposed system achieves centimeter-level localization accuracy, attaining a mean absolute error (MAE) of 3.92 cm, outperforming several representative learning-based VLP models under the same experimental settings. TLSNet also effectively suppresses large-error outliers, with 95% of positioning errors remaining below 9.47 cm across receiver heights up to 1.2 m.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"434-448"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433287","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

Simple Methods for Estimating Required SNR in Dual-Polarized MIMO Systems Under Limited XPD 有限XPD条件下双极化MIMO系统所需信噪比的简单估计方法

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-04-21 DOI: 10.1109/TBC.2026.3680043

Sungjun Ahn;Sung-Ik Park

{"title":"Simple Methods for Estimating Required SNR in Dual-Polarized MIMO Systems Under Limited XPD","authors":"Sungjun Ahn;Sung-Ik Park","doi":"10.1109/TBC.2026.3680043","DOIUrl":"https://doi.org/10.1109/TBC.2026.3680043","url":null,"abstract":"This paper presents simple semi-analytical methods for estimating the required carrier-to-noise ratio (CNR) in multi-input multi-output (MIMO) broadcasting systems. The estimates are derived according to cross-polarization discrimination (XPD) level of the transmission channel, thereby evaluating the impact of cross-polarization interference. The proposed models convert known single-input single-output (SISO) system performance, e.g., CNR threshold measurements in ATSC A/327 [Annex A, 1], into the CNR required in MIMO transmission. Two classes of estimation are developed: one assuming perfect channel knowledge (Class P), and the other accounting for channel estimation errors and pilot boosting (Class E). In addition to the conventional single-layer configuration, estimations under layered division multiplexing (LDM) configurations are also addressed, where every layer is assumed to use MIMO. Closed-form expressions are provided for both layered and non-layered MIMO scenarios. The models offer a lightweight alternative to full-system simulations, enabling early-stage design evaluation and practical system planning in cross-polarized environments.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"734-753"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11488736","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433360","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

DI-PCQA: Domain-Invariant Learning Framework for No-Reference Point Cloud Quality Assessment 无参考点云质量评估的领域不变学习框架

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-05 DOI: 10.1109/TBC.2026.3689330

Biqi Wu;Xiwu Shang;Xiaoli Zhao;Guozhong Wang;Yifan Zuo

{"title":"DI-PCQA: Domain-Invariant Learning Framework for No-Reference Point Cloud Quality Assessment","authors":"Biqi Wu;Xiwu Shang;Xiaoli Zhao;Guozhong Wang;Yifan Zuo","doi":"10.1109/TBC.2026.3689330","DOIUrl":"https://doi.org/10.1109/TBC.2026.3689330","url":null,"abstract":"Recent advances in no-reference point cloud quality assessment (NR-PCQA) have been largely driven by deep learning techniques. To further enhance prediction accuracy, existing methods often rely on multi-modal feature fusion. However, the introduction of multimodality ignores the consideration of the model’s generalization ability. Moreover, it often introduces additional computational overhead inference. To address these challenges, we propose a novel NR-PCQA method based on domain-invariant learning framework (DI-PCQA). Specifically, the input point cloud is first projected to obtain a spatial-domain visual representation. To construct different representations with the same origin and semantics, this spatial information is further transformed into a frequency domain representation. Both spatial and frequency features are then fed into a weight-shared encoder to extract feature. Subsequently, an adversarial domain classification task is introduced. We attach the gradient reversal layer to the weight-shared encoder, enabling the encoder to generate features that can confuse the domain classifier. This adversarial training encourages the weight-shared encoder to learn domain-invariant semantic features rather than overfitting to a single data distribution. And the spatial features are simultaneously regressed to quality scores. Finally, DI-PCQA employs a decoupled training-testing strategy. It leverages both spatial and frequency domain inputs to learn domain-invariant representations during training, but performs efficient quality prediction using only spatial information at test time. We conduct extensive tests on public databases and achieve superior performance. Further evaluations confirm its generalization ability and fast inference speed.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"558-569"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433385","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

NSGR: A Tile-Free 360° Video LIVE System With Edge Servers Enhancing Viewport Quality NSGR：一个无贴片360°视频直播系统与边缘服务器增强视口质量

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-29 DOI: 10.1109/TBC.2026.3696566

Nianzhen Gao;Ting Bi;Zui Tao;Fangzheng Feng;Tao Jiang

{"title":"NSGR: A Tile-Free 360° Video LIVE System With Edge Servers Enhancing Viewport Quality","authors":"Nianzhen Gao;Ting Bi;Zui Tao;Fangzheng Feng;Tao Jiang","doi":"10.1109/TBC.2026.3696566","DOIUrl":"https://doi.org/10.1109/TBC.2026.3696566","url":null,"abstract":"In 360° video transmission, the “tile-based” mode has proven effective in enhancing viewport quality under bandwidth constraints. However, this mode cannot support real-time generation and large-scale distribution of multi-bitrate and multi-tile video versions within the latency limits required for live streaming. This paper introduces a “tile-free” mode to reduce encoding redundancy and lower computation, caching, and communication overhead. With this mode, we design an edge-assisted LIVE system for processing and transmitting 360° videos, where effective signal superposition of the video layers enables users with poor channel quality to achieve a high quality of experience (QoE). The joint strategy of user grouping, bandwidth allocation, and bitrate decision is formulated as a multi-objective mixed-integer nonlinear programming problem to maximize the users’ QoE and minimize the system energy consumption. After deriving the latency equivalence properties among non-dominated solutions, we propose the Non-dominated Sorting Genetic Relaxation (NSGR) scheme, which leverages enhanced genetic algorithms with continuous slackness for improved optimization. Compared with state-of-the-art schemes, NSGR makes 360° video LIVE feasible, enhancing total QoE by <inline-formula> <tex-math>$18sim ~23%$ </tex-math></inline-formula> and reducing the energy consumption by <inline-formula> <tex-math>$50sim ~62%$ </tex-math></inline-formula>. Additionally, we propose a constant-time complexity alternative that achieves similar gain.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"495-504"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433390","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

Architecture for Efficient Local Content Insertion in ATSC 3.0 SFNs atsc3.0 sfn中高效本地内容插入的体系结构

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2025-11-07 DOI: 10.1109/TBC.2025.3627776

Orlando Landrove;Erick Jimenez;Pablo Angueira;Iñaki Eizmendi;Willie Perez;Jon Montalban

引用次数: 0