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Subjective and Objective Quality Assessment of Multi-Attribute Retouched Face Images IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-27 Guanghui Yue, Honglv Wu, Weiqing Yan, Tianwei Zhou, Hantao Liu, Wei Zhou
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Fast Transform Kernel Selection Based on Frequency Matching and Probability Model for AV1 IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-26 Zhijian Hao, Heming Sun, Guohao Xu, Jiaming Liu, Xiankui Xiong, Xuanpeng Zhu, Xiaoyang Zeng, Yibo Fan
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Deep Learning Approach for No-Reference Screen Content Video Quality Assessment IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-26 Ngai-Wing Kwong, Yui-Lam Chan, Sik-Ho Tsang, Ziyin Huang, Kin-Man Lam
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Diversity Receiver for ATSC 3.0-in-Vehicle: Design and Field Evaluation in Metropolitan SFN IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-26 Sungjun Ahn, Bo-Mi Lim, Sunhyoung Kwon, Sungho Jeon, Xianbin Wang, Sung-Ik Park
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Synergistic Temporal-Spatial User-Aware Viewport Prediction for Optimal Adaptive 360-Degree Video Streaming IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-21 Yumei Wang, Junjie Li, Zhijun Li, Simou Shang, Yu Liu
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ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-21 Axi Niu, Trung X. Pham, Kang Zhang, Jinqiu Sun, Yu Zhu, Qingsen Yan, In So Kweon, Yanning Zhang
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JUST360: Optimizing 360-Degree Video Streaming Systems With Joint Utility IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-21 Zhijun Li, Yumei Wang, Yu Liu, Junjie Li, Ping Zhu
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An Innovative Adaptive Web-Based Solution for Improved Remote Co-Creation and Delivery of Artistic Performances IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-13 Mohammed Amine Togou, Anderson Augusto Simiscuka, Rohit Verma, Noel E. O’Connor, Iñigo Tamayo, Stefano Masneri, Mikel Zorrilla, Gabriel-Miro Muntean
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IEEE Transactions on Broadcasting Publication Information IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-05
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IEEE Transactions on Broadcasting Information for Authors IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-03-05
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Performance Assessment for LDM Transmission Based on DVB Satellite Standard IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-02-29 Pansoo Kim, Hyuncheol Park
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Enhancing User Experience in Ultra HD Cloud Performing Arts Live Streaming: A QoS-to-QoE Mapping Approach IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-02-22 Li Yang, Jianzhang Liu, Shufeng Li, Deyou Zhang, Zhiping Xia
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Spherical Distortion Temporal Propagation and Spatial Mapping Model for Efficient Panoramic Video Coding IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-02-19 Xu Yang, Minfeng Huang, Hongwei Guo, Shengxi Li, Lei Luo, Ce Zhu
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Learning-Based Fast Splitting and Directional Mode Decision for VVC Intra Prediction IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-02-19 Yuanyuan Huang, Junyi Yu, Dayong Wang, Xin Lu, Frederic Dufaux, Hui Guo, Ce Zhu
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Time-Robust MRC Design for Broadcasting Reception Enhancement IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-02-07 Wenbo Guo, Hongzhi Zhao, Jiaxin Du, Shihai Shao, Youxi Tang
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Transformer-Based Light Field Geometry Learning for No-Reference Light Field Image Quality Assessment IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-31 Lili Lin, Siyu Bai, Mengjia Qu, Xuehui Wei, Luyao Wang, Feifan Wu, Biao Liu, Wenhui Zhou, Ercan Engin Kuruoglu
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HDIQA: A Hyper Debiasing Framework for Full Reference Image Quality Assessment IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-31 Mingliang Zhou, Heqiang Wang, Xuekai Wei, Yong Feng, Jun Luo, Huayan Pu, Jinglei Zhao, Liming Wang, Zhigang Chu, Xin Wang, Bin Fang, Zhaowei Shang
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Occupancy-Assisted Attribute Artifact Reduction for Video-Based Point Cloud Compression IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-30 Linyao Gao, Zhu Li, Lizhi Hou, Yiling Xu, Jun Sun
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High Accuracy Channel Estimation With TxID Sequence in ATSC 3.0 SFN IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-30 Zhihong Hunter Hong, Yiyan Wu, Wei Li, Liang Zhang, Zhiwen Zhu, Sung-Ik Park, Namho Hur, Eneko Iradier, Jon Montalban
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DMML: Deep Multi-Prior and Multi-Discriminator Learning for Underwater Image Enhancement IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-25 Alireza Esmaeilzehi, Yang Ou, M. Omair Ahmad, M. N. S. Swamy
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Access Optimization in 802.11ax WLAN for Load Balancing and Competition Avoidance of IPTV Traffic IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-25 Sujie Shao, Linlin Zhang, Fei Qi
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Low-Rate LDPC Code Design for DTMB-A IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-22 Zhitong He, Kewu Peng, Chao Zhang, Jian Song
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GCOTSC: Green Coding Techniques for Online Teaching Screen Content Implemented in AVS3 IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-10 Liping Zhao, Zhuge Yan, Zehao Wang, Xu Wang, Keli Hu, Huawen Liu, Tao Lin
During and following the global COVID-19 pandemic, the use of screen content coding applications such as large-scale cloud office, online teaching, and teleconferencing has surged. The vast amount of online data generated by these applications, especially online teaching, has become a vital source of Internet video traffic. Consequently, there is an urgent need for low-complexity online teaching screen
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Retina-U: A Two-Level Real-Time Analytics Framework for UHD Live Video Streaming IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-10 Wei Zhang, Yunpeng Jing, Yuan Zhang, Tao Lin, Jinyao Yan
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EffiHDR: An Efficient Framework for HDRTV Reconstruction and Enhancement in UHD Systems IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-10 Hengsheng Zhang, Xueyi Zou, Guo Lu, Li Chen, Li Song, Wenjun Zhang
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Fast Decoding of Polar Codes for Digital Broadcasting Services in 5G IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-05 He Sun, Emanuele Viterbo, Bin Dai, Rongke Liu
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Quality-of-Experience Evaluation for Digital Twins in 6G Network Environments IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-05 Zicheng Zhang, Yingjie Zhou, Long Teng, Wei Sun, Chunyi Li, Xiongkuo Min, Xiao-Ping Zhang, Guangtao Zhai
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Omnidirectional Video Quality Assessment With Causal Intervention IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2024-01-03 Zongyao Hu, Lixiong Liu, Qingbing Sang
Spherical signals of omnidirectional videos need to be projected to a 2D plane for transmission or storage. The projection will produce geometrical deformation that affects the feature representation of Convolutional Neural Networks (CNN) on the perception of omnidirectional videos. Currently developed omnidirectional video quality assessment (OVQA) methods leverage viewport images or spherical CNN
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A Blind Video Quality Assessment Method via Spatiotemporal Pyramid Attention IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-12-28 Wenhao Shen, Mingliang Zhou, Xuekai Wei, Heqiang Wang, Bin Fang, Cheng Ji, Xu Zhuang, Jason Wang, Jun Luo, Huayan Pu, Xiaoxu Huang, Shilong Wang, Huajun Cao, Yong Feng, Tao Xiang, Zhaowei Shang
As social media communication develops, reliable multimedia quality evaluation indicators have become a prerequisite for enriching user experience services. In this paper, we propose a multiscale spatiotemporal pyramid attention (SPA) block for constructing a blind video quality assessment (VQA) method to evaluate the perceptual quality of videos. First, we extract motion information from the video
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Learning-Based Efficient Quantizer Selection for Fast HEVC Encoder IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-12-19 Motong Xu, Byeungwoo Jeon
The rate-distortion optimized quantization (RDOQ) in HEVC has improved the coding efficiency of the conventional uniform scalar quantization (SQ) very much. Since the RDOQ is computationally complex, in this paper, we investigate a way of performing RDOQ more efficiently in HEVC. Based on our statistical observation of non-trivial percentage of transform blocks (TB) for which RDOQ does not change their
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Federated Multitask Learning for Pedestrian Location-Aware 5G Multicast/Broadcast Services IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-12-15 Zexuan Jing, Junsheng Mu, Jian Jin, Zhenzhen Jiao, Peng Yu
5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time
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2023 Scott Helt Memorial Award for the Best Paper Published in the IEEE Transactions on Broadcasting IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-12-08
The 2023 Scott Helt Memorial Award was awarded to Hequn Zhang, Yue Zhang, John Cosmas, Nawar Jawad, Wei Li, Robert Muller, Tao Jiang for their paper, “mmWave Indoor Channel Measurement Campaign for 5G New Radio Indoor Broadcasting”. The papers appeared in the IEEE Transactions on Broadcasting, vol. 68, no. 2, pp. 331–344, June 2022. The purpose of the IEEE Scott Helt Memorial Award is to recognize
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Computationally Stable Low Sampling Rate Digital Predistortion for Non-Terrestrial Networks IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-12-05 Boyan Li, Xin Hu, Naixin Kan, Weidong Wang, Fadhel M. Ghannouchi
With the advent of the fifth generation (5G) New Radio (NR), the Non-Terrestrial Network (NTN) stands out as a solution to enable wider coverage of broadcast satellites. NTN systems require higher data rates and bandwidth. Digital predistortion (DPD) is commonly adopted as an effective method to enhance the power efficiency of broadcast satellites’ NTN systems. With the continuous increase of signal
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Virtual-Competitors-Based Rate Control for 360-Degree Video Coding IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-12-04 Jielian Lin, Hongbin Lin, Yiwen Xu, Yuanxun Kang, Tiesong Zhao
360 video applications are attracting more attention in broadcasting systems. In the case of limited bandwidth, the bit fluctuation will affect the perception quality when transmitting 360-degree videos. To further optimize the bit allocation and reduce fluctuation, this paper proposes a virtual-competitors-based Rate Control (RC) algorithm for 360-degree video coding. The virtual competitors’ concept
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Modeling and Analysis of Broadcast-Cellular Converged Networks for Hybrid Live and On-Demand Services IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-11-29 Wenjun Zhang, Yeyue Cai, Meixia Tao, Zhiyong Chen, Dazhi He, Yin Xu, Yunfeng Guan
The broadcast-cellular converged network presents a promising architecture for meeting the increasing demand for high-bandwidth and low-latency services while alleviating traffic burdens. In this paper, we consider a broadcast-cellular converged network to provide both live streaming and on-demand services. In this converged network, the broadcast network is responsible for delivering live streaming
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Misaligned RGB-Depth Boundary Identification and Correction for Depth Image Recovery IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-11-29 Meng Yang, Lulu Zhang, Delong Suzhang, Ce Zhu, Nanning Zheng
Raw depth images generally contain a large number of erroneous pixels near object boundaries due to the limitation of depth sensors. It induces misalignment of object boundaries between RGB and depth pairs. Most existing methods do not explicitly study such RGB-Depth misalignment problem. Thereby, depth boundaries cannot be accurately recovered. In this paper, a simple yet effective model is developed
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End-to-End Transformer for Compressed Video Quality Enhancement IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-11-29 Li Yu, Wenshuai Chang, Shiyu Wu, Moncef Gabbouj
Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatio-temporal information of adjacent frames mainly by deformable convolution. However, the CNN-based methods can only exploit local information, thus lacking the exploration of global information. Moreover, current methods enhance the video
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UAV-Relay-Assisted Live Layered Video Multicast for Cell-Edge Users in NOMA Networks IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-11-09 Hang Shen, Ziyuan Tong, Tianjing Wang, Guangwei Bai
In this paper, a live layered video multicast framework based on unmanned aerial vehicle (UAV) relays is presented for on-orthogonal multiple access (NOMA) networks, aiming to maximize the aggregated video reception quality for cell-edge users. A visualizable graph model is constructed to characterize the coupling of the deployment of UAVs and their association with multicast groups with interference
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Design and Evaluation of Multi-Layer NOMA on NR Physical Layer for 5G and Beyond IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-11-09 Md Shantanu Islam, Raouf Abozariba, De Mi, Mohammad N. Patwary, Dazhi He, A. Taufiq Asyhari
This paper investigates the integration of multi-layer non-orthogonal multiple access (N-NOMA) into a 5G New Radio (NR) compliant transceiver model, aiming to reveal the full potential of the NOMA technology in practical scenarios. We propose an N-NOMA-aided 5G NR physical layer (PHY) design, where a simplified multi-layer NOMA multiplexer with a one-shot multiplexing technique is developed to reduce
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On the Design of Companding-Based Scheme for PAPR Reduction in OFDM Systems IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-11-07 Yang Zhang, Jun Hou, Leibo Wang, Huaijie Liu
Companding is an effective method for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing. In this paper, the universal design criterion of companding scheme is introduced by analyzing signal distribution transformation and corresponding signal distortion. In addition, according to the constraints of the proposed criterion, a new companding scheme is designed
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Resource Efficient Full-Duplex Mode of Transmissions Under Imperfect CSI IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-10-26 Merhawit Berhane Teklu, Dong-You Choi, Weixiao Meng
To enable spectrum-efficient and energy-efficient technology, this article analyzes a full-duplex (FD) assisted multiuser-MIMO (MU-MIMO) system which affected by imperfect channel state information (ICSI) for a Rician fading environment. This system suffers from co-channel interference, multi-user interferences, multiple stream interferences, residual Self-interference, and noise. To combat interferences
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Multicast and Unicast Superposition Transmission in MIMO OFDMA Systems With Statistical CSIT IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-26 Yong Jin Daniel Kim, David Vargas
We consider a downlink multicast and unicast superposition transmission in multi-layer Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiple Access (OFDMA) systems when only the statistical channel state information is available at the transmitter (CSIT). Multiple users can be scheduled by using the time/frequency resources in OFDMA, while for each scheduled user MIMO spatial
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Integrates Spatiotemporal Visual Stimuli for Video Quality Assessment IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-26 Wenzhong Guo, Kairui Zhang, Xiao Ke
While feature extraction employing pre-trained models proves effective and efficient for no-reference video tasks, it falls short of adequately accounting for the intricacies of the Human Visual System (HVS). In this study, we proposed a novel approach to Integration of spatio-temporal Visual Stimuli into Video Quality Assessment (IVS-VQA) for the inaugural time. Exploiting the heightened sensitivity
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Decomposed Vector Combination-Based Low-Complexity Behavioral Model for Digital Predistortion of RF Transmitters IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-22 Renlong Han, Chengye Jiang, Guichen Yang, Qianqian Zhang, Falin Liu
In this paper, we present a novel behavioral modeling technique based on decomposed vector combination (DVC) for digital predistortion (DPD) of RF Transmitters. The basis function of the proposed DVC model consists of a piecewise function-based magnitude term and a linear phase-combination-based phase term. The novel DVC basis functions are still linear-in-parameters and go beyond the classical Volterra
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Artificial Intelligence Aided Low Complexity RRM Algorithms for 5G-MBS IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-20 Ernesto Fontes Pupo, Claudia Carballo González, Jon Montalban, Pablo Angueira, Maurizio Murroni, Eneko Iradier
For the upcoming 5G-Advanced, the multicast/broadcast services (5G-MBS) capability is one of the most appealing use cases. The effective integration of point-to-multipoint communication will address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper proposes novel approaches for the dynamic access technique selection and
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Reconfigurable Intelligent Surface-Enhanced Layered Division Multiplexing for 5G-Based MBMS Over Vehicle Networks IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-19 Qiang Liu, Da Chen, Yijian Liu, Zhonghui Zhao
The layered division multiplexing (LDM) for 5G-based multimedia broadcast multicast service (MBMS) offers a significant approach for vehicle networks to diverse quality of service (QoS) and improve spectrum efficiency. In this paper, we present a reconfigurable intelligent surface (RIS)-enhanced LDM system for 5G-based MBMS to provide high-quality mixed unicast-broadcast services for the vehicle networks
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An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-18 Jian Wang, Zhongle Wu, Yulong Hao, Cheng Yang, Yafei Shi
For improving the accuracy and robustness of predicting radio propagation in the frequency bands of FM Broadcasting Services, we proposed a propagation prediction model suitable for multi-band and multi-scenario based on Support Vector Regression (SVR). The modeling is based on Beijing’s measurement data, covering various environments, such as rural, suburban, and urban areas, with a frequency range
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MFE-Net: A Multi-Layer Feature Extraction Network for No-Reference Quality Assessment of 3-D Point Clouds IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-18 Qihao Liang, Zhouyan He, Mei Yu, Ting Luo, Haiyong Xu
As an important data form, the point cloud (PC) can present real-world objects in a 3D and realistic way. In order to measure the performance of acquisition, processing, and compression systems, it is crucial to propose a PC quality assessment (PCQA) method. Nowadays, a lot of full-reference PCQA methods have been developed, but it is obviously difficult to have wide applications. Usually, we should
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A Full-Reference Image Quality Assessment Method via Deep Meta-Learning and Conformer IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-08 Shujun Lang, Xu Liu, Mingliang Zhou, Jun Luo, Huayan Pu, Xu Zhuang, Jason Wang, Xuekai Wei, Taiping Zhang, Yong Feng, Zhaowei Shang
In this paper, a full-reference image quality assessment (FR-IQA) model based on deep meta-learning and Conformer is proposed. We combine the Conformer architecture with a Siamese network to extract the feature vectors of the reference and distorted images and calculate the similarity of these feature vectors as the predicted score of the image. We use meta-learning to help the model identify different
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Hybrid Approach of Holography and Augmented-Reality Reconstruction Optimizations for Hyper-Reality Metaverse Video Applications IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-08 Hyoung Lee, Hakdong Kim, Taeheul Jun, Wookho Son, Cheongwon Kim, MinSung Yoon
In this study, we offer a new type of hyper-realistic holographic display system that simultaneously displays both holographic 3D images and AR images. The proposed ultra-realistic display combines 360-degree computer generated holographic (CGH) 3D content to be reconstructed from an SLM and AR content (2D image) to be spatially projected from a microdisplay, allowing users to watch a clearly blended
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Blind Perceptual Quality Assessment of LFI Based on Angular-Spatial Effect Modeling IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-09-06 Zhengyu Zhang, Shishun Tian, Yuhang Zhang, Wenbin Zou, Luce Morin, Lu Zhang
By recording scenes from multiple viewpoints, Light Field Image (LFI) encompasses both angular and spatial information, thereby offering users a more immersive experience. Since LFIs may be distorted at various stages from acquisition to visualization, Light Field Image Quality Assessment (LFIQA) is of vitally important to monitor the potential impairments of LFI quality. However, existing objective
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Angle-Based Multicast User Selection and Precoding for Beam-Hopping Satellite Systems IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-30 Tong Shi, Yuyang Liu, Shaoli Kang, Shaohui Sun, Rongke Liu
This paper deals with user selection, beam selection, and multicast precoding with full frequency reuse (FFR) for LEO satellite communication systems while exploiting the angle information defined by elevation and azimuth instead of the channel state information (CSI). LEO satellites employ large-scale phased array antennas (PAAs) that accommodate flexible networks, which makes it challenging to accomplish
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Vaser: Optimizing 360-Degree Live Video Ingest via Viewport-Aware Neural Enhancement IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-23 Zelong Wang, Zhenxiao Luo, Miao Hu, Min Chen, Di Wu
As a revolutionary technique, 360-degree live video streaming provides users with an immersive and realistic experience for a live event. However, due to the limited upload bandwidth of broadcasters, the ingest of 360-degree live video streams has become a bottleneck that seriously lowers the overall utility of viewers. In this paper, we first conduct a series of measurement studies to unveil the characteristics
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Optimal Multicasting in Dual mmWave/μ Wave 5G NR Deployments With Multi-Beam Directional Antennas IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-23 Olga Chukhno, Nadezhda Chukhno, Dmitri Moltchanov, Antonella Molinaro, Anna Gaydamaka, Andrey Samouylov, Yevgeni Koucheryavy, Antonio Iera, Giuseppe Araniti
The design of multicast services in the fifth-generation (5G) New Radio (NR) deployments is hampered by the directional nature of antenna radiation patterns. This complexity is further compounded by the emergence of new deployment options, such as dual millimeter wave (mmWave) and microwave $(\mu $ Wave) base station (BS) deployments, as well as new antenna design solutions. In this paper, the resource
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An Efficient and Adaptive Content Delivery System Based on Hybrid Network IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-21 Linjie Nie, Shuo Yang, Xinran Zheng, Xingjun Wang
Nowadays, Content Delivery Network (CDN) is widely used for its convenience in providing services. However, the increasing demand for bandwidth puts tremendous pressure on CDN. Inspired by the great potential of periodic broadcasting to save bandwidth, we suggest an Efficient and Adaptive Content Delivery System (EACDS) to decrease traffic and shorten video content delivery delays. Specifically, we
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Iterative Successive Nonlinear Self-Interference Cancellation for In-Band Full-Duplex Communications IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-01 Zhihong Hunter Hong, Liang Zhang, Yiyan Wu, Wei Li, Sung-Ik Park, Sungjun Ahn, Namho Hur, Eneko Iradier, Jon Montalban, Pablo Angueira
In-band full-duplex (IBFD) communications have recently been considered for wireless backhaul in the ultra-high frequency (UHF) band terrestrial broadcast systems since they can double the spectral efficiency compared to conventional half-duplex communications. The inherent challenge of IBFD communication is self-interference (SI), the power leakage from the co-located transmitter to the receiver.
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Architectures of 4x1 and 4x2 OFDM With Two-path Parallel ICI Cancellation Full-Rate Schemes in Mobile Channels IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-01 Hen-Geul Yeh, Seok-Chul Kwon
Further developing the idea of combining spatial diversity and parallel cancellation (PC) scheme together for mitigating intercarrier interference (ICI), we develop both 4x1 and 4x2 orthogonal frequency division multiplexing (OFDM) architectures with two-pair two-path PC schemes. First, the two-path PC scheme is integrated with the space-time-frequency (STF) block code OFDM transceiver to develop 4x1
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Machine Learning Assisted Video Stream Offloading for 5G MBMS Mobile Edge Computing IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-08-01 Junsheng Mu, Jian Jin, Xiaojun Jing, Ronghui Zhang, Peiying Zhang, Hailong Zhu
With the advancement of multimedia technology, 5G MBMS has become a promising technology that makes full and effective use of network resources to provide services. Mobile edge computing (MEC) is a key technology that brings important improvements to MBMS by utilizing contextual information such as multimedia, virtual reality, etc. However, the deployment of MEC may bring the latency to the network
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Full-Reference Image Quality Assessment: Addressing Content Misalignment Issue by Comparing Order Statistics of Deep Features IEEE Trans. Broadcast. (IF 4.5) Pub Date : 2023-07-28 Xingran Liao, Xuekai Wei, Mingliang Zhou, Sam Kwong
This letter aims to develop advanced full-reference image quality assessment (FR-IQA) models to evaluate content-misaligned image pairs, which are commonly encountered in image reconstruction tasks and texture synthesis tasks. Traditional FR-IQA models tend to be overly sensitive to content shifting and misalignment, thus deviating from subjective evaluations. Herein, we propose a deep order statistical