IEEE Transactions on Broadcasting最新文献_第7页

Broadcasting and 6G Converged Network Architecture 广播和 6G 融合网络架构

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-28 DOI: 10.1109/TBC.2024.3407482

Haojiang Li;Wenjun Zhang;Yin Xu;Dazhi He;Haoyang Li

引用次数: 0

A Content-Aware Full-Reference Image Quality Assessment Method Using a Gram Matrix and Signal-to-Noise 使用克矩阵和信噪比的内容感知全参考图像质量评估方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-28 DOI: 10.1109/TBC.2024.3410707

Shuqi Han;Yueting Huang;Mingliang Zhou;Xuekai Wei;Fan Jia;Xu Zhuang;Fei Cheng;Tao Xiang;Yong Feng;Huayan Pu;Jun Luo

{"title":"A Content-Aware Full-Reference Image Quality Assessment Method Using a Gram Matrix and Signal-to-Noise","authors":"Shuqi Han;Yueting Huang;Mingliang Zhou;Xuekai Wei;Fan Jia;Xu Zhuang;Fei Cheng;Tao Xiang;Yong Feng;Huayan Pu;Jun Luo","doi":"10.1109/TBC.2024.3410707","DOIUrl":"10.1109/TBC.2024.3410707","url":null,"abstract":"With the emergence of transformer-based feature extractors, the effect of image quality assessment (IQA) has improved, but its interpretability is limited. In addition, images repaired by generative adversarial networks (GANs) produce realistic textures and spatial misalignments with high-quality images. In this paper, we develop a content-aware full-reference IQA method without changing the original convolutional neural network feature extractor. First, image signal-to-noise (SNR) mapping is performed experimentally to verify its superior content-aware ability, and based on the SNR mapping of the reference image, we fuse multiscale distortion and normal image features according to a fusion strategy that enhances the informative area. Second, judging the quality of GAN-generated images from the perspective of focusing on content may ignore the alignment between pixels; therefore, we add a Gram-matrix-based texture enhancement module to boost the texture information between distorted and normal difference features. Finally, experiments on numerous public datasets prove the superior performance of the proposed method in predicting image quality.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1279-1291"},"PeriodicalIF":3.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510394","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

Securing Content Production Centers in 5G Broadcasting: Strategies and Technologies for Mitigating Cybersecurity Risks 确保 5G 广播中内容制作中心的安全：降低网络安全风险的策略和技术

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-27 DOI: 10.1109/TBC.2024.3407596

Yang Liu;Jie Wang;Ruohan Cao;Yueming Lu;Yaojun Qiao;Yuanqing Xia;Daoqi Han

引用次数: 0

Cross-Dimensional Attention Fusion Network for Simulated Single Image Super-Resolution 用于模拟单张图像超级分辨率的跨维注意力融合网络

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-25 DOI: 10.1109/TBC.2024.3408643

Jingbo He;Xiaohai He;Shuhua Xiong;Honggang Chen

{"title":"Cross-Dimensional Attention Fusion Network for Simulated Single Image Super-Resolution","authors":"Jingbo He;Xiaohai He;Shuhua Xiong;Honggang Chen","doi":"10.1109/TBC.2024.3408643","DOIUrl":"10.1109/TBC.2024.3408643","url":null,"abstract":"Single image super-resolution (SISR) is a task of reconstructing high-resolution (HR) images from low-resolution (LR) images, which are obtained by some degradation process. Deep neural networks (DNNs) have greatly advanced the frontier of image super-resolution research and replaced traditional methods as the de facto standard approach. The attention mechanism enables the SR algorithms to achieve breakthrough performance after another. However, limited research has been conducted on the interaction and integration of attention mechanisms across different dimensions. To tackle this issue, in this paper, we propose a cross-dimensional attention fusion network (CAFN) to effectively achieve cross-dimensional inter-action with long-range dependencies. Specifically, the proposed approach involves the utilization of a cross-dimensional aggrega-tion module (CAM) to effectively capture contextual information by integrating both spatial and channel importance maps. The design of information fusion module (IFM) in CAM serves as a bridge for parallel dual-attention information fusion. In addition, a novel memory-adaptive multi-stage (MAMS) training method is proposed. We perform warm-start retraining with the same setting as the previous stage, without increasing memory consumption. If the memory is sufficient, we finetune the model with a larger patch size after the warm-start. The experimental results definitively demonstrate the superior performance of our cross-dimensional attention fusion network and training strategy compared to state-of-the-art (SOTA) methods, as evidenced by both quantitative and qualitative metrics.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"909-923"},"PeriodicalIF":3.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510396","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 VMAF: A Deep Neural Network-Based Approach to Blind Video Quality Assessment 无参照 VMAF：基于深度神经网络的盲目视频质量评估方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-19 DOI: 10.1109/TBC.2024.3399479

Axel De Decker;Jan De Cock;Peter Lambert;Glenn Van Wallendael

{"title":"No-Reference VMAF: A Deep Neural Network-Based Approach to Blind Video Quality Assessment","authors":"Axel De Decker;Jan De Cock;Peter Lambert;Glenn Van Wallendael","doi":"10.1109/TBC.2024.3399479","DOIUrl":"10.1109/TBC.2024.3399479","url":null,"abstract":"As the demand for high-quality video content continues to rise, accurately assessing the visual quality of digital videos has become more crucial than ever before. However, evaluating the perceptual quality of an impaired video in the absence of the original reference signal remains a significant challenge. To address this problem, we propose a novel No-Reference (NR) video quality metric called NR-VMAF. Our method is designed to replicate the popular Full-Reference (FR) metric VMAF in scenarios where the reference signal is unavailable or impractical to obtain. Like its FR counterpart, NR-VMAF is tailored specifically for measuring video quality in the presence of compression and scaling artifacts. The proposed model utilizes a deep convolutional neural network to extract quality-aware features from the pixel information of the distorted video, thereby eliminating the need for manual feature engineering. By adopting a patch-based approach, we are able to process high-resolution video data without any information loss. While the current model is trained solely on H.265/HEVC videos, its performance is verified on subjective datasets containing mainly H.264/AVC content. We demonstrate that NR-VMAF outperforms current state-of-the-art NR metrics while achieving a prediction accuracy that is comparable to VMAF and other FR metrics. Based on this strong performance, we believe that NR-VMAF is a viable approach to efficient and reliable No-Reference video quality assessment.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"844-861"},"PeriodicalIF":3.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940837","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

Multi-Reference-Based Cross-Scale Feature Fusion for Compressed Video Super Resolution 基于多参考的跨尺度特征融合，实现压缩视频超分辨率

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-13 DOI: 10.1109/TBC.2024.3407517

Lu Chen;Mao Ye;Luping Ji;Shuai Li;Hongwei Guo

{"title":"Multi-Reference-Based Cross-Scale Feature Fusion for Compressed Video Super Resolution","authors":"Lu Chen;Mao Ye;Luping Ji;Shuai Li;Hongwei Guo","doi":"10.1109/TBC.2024.3407517","DOIUrl":"10.1109/TBC.2024.3407517","url":null,"abstract":"To save transmission bandwidth, there exists an approach to down-sample a video and then up-sample the compressed video to save bit rates. The existing Super Resolution (SR) methods generally design powerful networks to compensate the loss information introduced by down-sampling. But the information of entire video is not fully utilized and effectively fused, resulting in the learned context information that is not enough for the high quality reconstruction. We propose a multi high-quality frames Referenced Cross-scale compressed Video Super Resolution method (RCVSR) that wisely uses past-and-future information, to pursue higher compression efficiency. Specifically, a joint reference motion alignment module is proposed. Low resolution (LR) frame after up-sampling is separately aligned with past-and-future reference frames to preserve more spatial details; at the same time this LR frame is aligned with neighborhood frames to get continuous motion information and similar contents. Then, a reference based refinement module is applied to compensate motion and lost texture details by computing similarity matrix across channel dimensions. Finally, an attention guided dual-branch residual module is employed to enhance the reconstructed result concurrently. Compared with the HEVC anchor, the average gain of Bjontegaard Delta Rate (BD-Rate) under the Low-Delay-P (LDP) setting is 24.86%. In addition, an experimental comparison is made with the advanced SR methods and compressed video quality enhancement (VQE) methods, and the superior efficiency and generalization of the proposed algorithm are further reported.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"895-908"},"PeriodicalIF":3.2,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940838","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

Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks 用于室内广播网络的波分复用可见光通信系统的能效优化方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-13 DOI: 10.1109/TBC.2024.3407606

Dayu Shi;Xun Zhang;Ziqi Liu;Xuanbang Chen;Jianghao Li;Xiaodong Liu;William Shieh

{"title":"Energy Efficiency Optimization Method of WDM Visible Light Communication System for Indoor Broadcasting Networks","authors":"Dayu Shi;Xun Zhang;Ziqi Liu;Xuanbang Chen;Jianghao Li;Xiaodong Liu;William Shieh","doi":"10.1109/TBC.2024.3407606","DOIUrl":"10.1109/TBC.2024.3407606","url":null,"abstract":"This paper introduces a novel approach to optimize energy efficiency in wavelength division multiplexing (WDM) Visible Light Communication (VLC) systems designed for indoor broadcasting networks. A physics-based LED model is integrated into system energy efficiency optimization, enabling quantitative analysis of the critical issue of VLC energy efficiency: the nonlinear interplay between illumination and communication performance. The optimization jointly incorporates constraints on communication quality of each channel, and illumination performance, standardized by the International Commission on Illumination (CIE). The formulated nonlinear optimization problem is solved by the Sequential Quadratic Programming (SQP) algorithm in an experiment-based simulation. An integrated Red-Green-Blue-Yellow Light Emitting Diode (RGBY-LED) is measured for model calibration and three different scenarios are simulated to evaluate the generality of the proposed method. Results demonstrate a double enhancement in performance and a high versatility in accommodating various scenarios. Furthermore, it highlights the importance of balancing communication and illumination imperatives in VLC systems, challenging conventional perceptions focused solely on minimizing power consumption.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1207-1220"},"PeriodicalIF":3.2,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940839","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 Sobolev Norm-Based Variational Approach to Companding for PAPR Reduction in OFDM Systems 一种基于索波列夫规范的变分法，用于在 OFDM 系统中进行压缩以降低 PAPR

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-12 DOI: 10.1109/TBC.2024.3405346

Stephen DelMarco

{"title":"A Sobolev Norm-Based Variational Approach to Companding for PAPR Reduction in OFDM Systems","authors":"Stephen DelMarco","doi":"10.1109/TBC.2024.3405346","DOIUrl":"10.1109/TBC.2024.3405346","url":null,"abstract":"In this paper we present a new approach to high-performance compander design to reduce the peak-to-average power ratio (PAPR) that typically occurs in orthogonal frequency division multiplexing (OFDM) systems. Whereas many current compander designs assume a parametric model for the form of the transformed Rayleigh amplitude distribution, we define a constrained optimization problem for the functional form of the transformed distribution. We determine an optimal distribution which minimally deviates from the Rayleigh distribution and use a Sobolev norm to quantify distance. Use of the Sobolev norm imposes smoothness constraints on the transformed distribution, which are associated with lower out-of-band interference levels. We incorporate Lagrange multipliers into the problem formulation to enforce the constant power and probability density function constraints. We solve the constrained optimization problem using techniques from the Calculus of Variations and discuss compander and decompander design. We investigate the effect of incorporating derivative information, into the optimization formulation, on compander performance. We demonstrate compander performance through numerical simulation and compare compander performance to performance from a state-of-the-art variational compander which does not use derivative information in the formulation. We demonstrate performance improvements in out-of-band power rejection using the new compander.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"955-962"},"PeriodicalIF":3.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940840","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 Information for Authors 电气和电子工程师学会（IEEE）《关于广播作者信息的论文集》（IEEE Transactions on Broadcasting Information for Authors

IF 4.5 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-07 DOI: 10.1109/TBC.2024.3408433

引用次数: 0

IEEE Transactions on Broadcasting Publication Information 电气和电子工程师学会《广播学报》出版信息

IF 4.5 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2024-06-07 DOI: 10.1109/TBC.2024.3408431

引用次数: 0