IEEE Transactions on Broadcasting最新文献

{"title":"IEEE Transactions on Broadcasting Information for Readers and Authors","authors":"","doi":"10.1109/TBC.2025.3640761","DOIUrl":"https://doi.org/10.1109/TBC.2025.3640761","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"C3-C4"},"PeriodicalIF":4.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766222","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":"Successive Refinement Coding and SWIPT for Energy-Efficient Cooperative NOMA Systems","authors":"Kewen Wang;Meng Cheng","doi":"10.1109/TBC.2025.3635396","DOIUrl":"https://doi.org/10.1109/TBC.2025.3635396","url":null,"abstract":"Successive refinement (SR) coding has been integrated into cooperative non-orthogonal multiple access (CO-NOMA) to improve transmission efficiency. This paper further incorporates simultaneous wireless information and power transfer (SWIPT) into the SR-based CO-NOMA framework, enabling the relay user to operate using harvested energy and thereby reducing system power consumption. Assuming block Rayleigh fading channels, closed-form expressions for the outage probability are derived. The power allocation ratio and power splitting factor are jointly optimized to enhance system performance. Under a fixed total transmit power constraint, the proposed SWIPT-enabled system achieves significantly lower outage probabilities than the conventional CO-NOMA counterpart without SWIPT. The performance gain is further evaluated across different relay locations.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"382-386"},"PeriodicalIF":4.8,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362481","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":"DM-VSR: Depth-Aware Diffusion Models With Adaptive Modulation for Video Super-Resolution","authors":"Linlin Liu;Yifan Wang;Yize Wang;Zhen Xu;Jun Tang;Yong Ding","doi":"10.1109/TBC.2025.3637713","DOIUrl":"https://doi.org/10.1109/TBC.2025.3637713","url":null,"abstract":"Video Super-Resolution (VSR) is essential for enhancing the quality of low-resolution (LR) videos in practical applications. Recent studies have explored diffusion models (DMs) for VSR due to their ability to generate realistic details. However, existing methods overlook spatiotemporal object-scale variations and dynamic control demands during denoising. This leads to visual distortion and quality degradation, severely limiting its practical applications. To address these limitations, we propose <bold>DM-VSR</b>, a novel DM-based framework that incorporates depth-aware guidance and adaptive modulation for precise content reconstruction. Specifically, a Depth-aware Multimodal Fusion (DMF) module integrates depth maps, LR inputs, and flow-warped frames to provide unified depth-aware guidance. A Timestep Adaptive Modulation (TAM) module dynamically adjusts the control feature injection according to demand at each denoising step. Additionally, a Dynamic Consistency Loss (DCL) is introduced to align training objectives with the evolving semantic focus. Extensive experiments on the REDS4 and Vid4 benchmarks demonstrate that DM-VSR achieves competitive performance, surpassing state-of-the-art methods in both perceptual quality and temporal consistency. Moreover, DM-VSR generates more visually realistic results, emphasizing its effectiveness in real-world applications. The code will be released at <uri>https://github.com/aigcvsr/DM-VSR</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"329-344"},"PeriodicalIF":4.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362568","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":"Anime4K: A Hybrid CNN–Transformer Network for Anime Super-Resolution","authors":"Qi Liu;Feifan Cai;Zihao Zhang;Haiqi Zhu;Youdong Ding","doi":"10.1109/TBC.2025.3622413","DOIUrl":"https://doi.org/10.1109/TBC.2025.3622413","url":null,"abstract":"Although anime super-resolution (SR) has garnered increasing interest in recent years, many existing approaches still inherit design principles from photorealistic imagery, overlooking the unique visual traits of anime. In this work, we revisit the anime SR task from an anime-specific perspective and propose a unified solution tailored for real-world restoration. First, we present A4K, the first 4K-resolution dataset specifically curated for anime SR, built via a dual-criterion selection pipeline combining perceptual quality and structural complexity, resulting in cleaner and more informative training samples. Second, we introduce AniFusionNet, a hybrid CNN–Transformer architecture that dynamically fuses local convolutional features with global self-attention, effectively balancing fine detail reconstruction and global coherence. Finally, we introduce a targeted ground truth (GT) enhancement strategy that selectively strengthens hand-drawn line structures, enabling more accurate learning of anime-specific textures. Extensive experiments on public benchmarks demonstrate that our approach achieves state-of-the-art performance in edge sharpness, color consistency, and artifact suppression. The project is available at <uri>https://github.com/wasai67/Anime4K</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"345-360"},"PeriodicalIF":4.8,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362498","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":"Blind Light Field Image Quality Assessment Using Multiplane Texture and Multilevel Wavelet Information","authors":"Zhengyu Zhang;Shishun Tian;Jianjun Xiang;Wenbin Zou;Luce Morin;Lu Zhang","doi":"10.1109/TBC.2025.3627787","DOIUrl":"https://doi.org/10.1109/TBC.2025.3627787","url":null,"abstract":"Light Field Image (LFI) has garnered remarkable interest and fascination due to its burgeoning significance in immersive applications. Although the abundant information in LFIs enables a more immersive experience, it also poses a greater challenge for Light Field Image Quality Assessment (LFIQA), especially when reference information is inaccessible. In this paper, inspired by the holistic visual perception of high-dimensional LFIs and neuroscience studies on the Human Visual System (HVS), we propose a novel Blind Light Field image quality assessment metric by exploring MultiPlane Texture and Multilevel Wavelet Information, abbreviated as MPT-MWI-BLiF. Specifically, considering the texture sensitivity of the secondary visual cortex (V2), we first convert LFIs into multiple individual planes and capture textural variations from these planes. Then, the statistical histogram of textural variations for all planes is calculated as holistic textural variation features. In addition, motivated by the fact that neuronal responses in the visual cortex are frequency-dependent, we simulate this visual perception process by decomposing LFIs into multilevel wavelet subbands with Four-Dimensional Discrete Haar Wavelet Transform (4D-DHWT). After that, the subband geometric features of first-level 4D-DHWT subbands and the coefficient intensity features of second-level 4D-DHWT subbands are computed respectively. Finally, we combine all the extracted quality-aware features and employ the widely-used Support Vector Regression (SVR) to predict the perceptual quality of LFIs. To fully validate the effectiveness of the proposed metric, we perform extensive experiments on five representative LFIQA databases with two cross-validation methods. Experimental results demonstrate the superiority of the proposed metric in quality evaluation, as well as its low time complexity compared to other state-of-the-art metrics. The full code will be publicly available at <uri>https://github.com/ZhengyuZhang96/MPT-MWI-BLiF</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"1092-1107"},"PeriodicalIF":4.8,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765632","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":"IRBFusion: Diffusion-Based Blind Image Super Resolution Using Unsupervised Learning and Bank of Restoration Networks","authors":"Morteza Poudineh;Alireza Esmaeilzehi;M. Omair Ahmad","doi":"10.1109/TBC.2025.3622337","DOIUrl":"https://doi.org/10.1109/TBC.2025.3622337","url":null,"abstract":"Image super resolution focuses on increasing the spatial resolution of low-quality images and enhancing their visual quality. Since the image degradation process is unknown in real-life scenarios, it is crucial to perform image super resolution in a blind manner. Diffusion models have revolutionized the task of blind image super resolution in view of their powerful capability of producing realistic textures and structures. Design of the condition network is a key factor for diffusion models in providing high image super resolution performances. In this regard, we develop an effective image restoration bank by using a three-stage learning algorithm based on the idea of unsupervised learning, and feed its results, wherein visual artifacts are remarkably suppressed, to the condition network. The use of the unsupervised learning in the design of our image restoration bank guarantees that both diverse contextual information of visual signals, as well as, different degradation operations are considered for the task of blind image super resolution. Further, we guide the feature generation process of the condition network in such a way that the fidelity of the feature tensors produced for the task of image super resolution remains high. The results of extensive experiments show the superiority of our method over the state-of-the-art blind image super resolution schemes in the case of various benchmark datasets.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"1048-1064"},"PeriodicalIF":4.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766209","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":"Geographic Segmented Localcasting Co-Channel Interference Mitigation Using Iterative Joint Detection and Decoding","authors":"Hao Ju;Yin Xu;Dazhi He;Haoyang Li;Wenjun Zhang;Yiyan Wu","doi":"10.1109/TBC.2025.3579222","DOIUrl":"https://doi.org/10.1109/TBC.2025.3579222","url":null,"abstract":"Geographic Segmented Localcasting (GSL) is an emerging Digital Terrestrial Television Broadcast (DTTB) physical layer operating mode. The system utilizes LDM-SFN to enable both wide-area Single Frequency Network (SFN) coverage and localized broadcast/multicast services within a single Radio Frequency (RF) broadcast channel. By combining SFN (core layer) and localcasting (enhanced layer) via Layered Division Multiplexing (LDM), GSL improves spectrum efficiency but faces challenges such as co-channel interference and SFN vs. Localcasting Channel Profile Mismatch (LCPM), which limit localcasting coverage. This paper presents two receiving methods that facilitate LDPC-coded LDM signal reception. Method 1 is the Multiple localcasting signals Iterative Joint Detection and Decoding (IJDD), which can mitigate severe co-channel interference with required Channel Status Information (CSI) of nearby localcasting transmitters. Method 2 is the Constellation Rotated IJDD (CR-IJDD), which can mitigate severe LCPM without CSI. The proposed methods enable decoding of both desired and interfering signals under high SNR conditions, enhancing spectrum reuse. Additionally, an Early Extrinsic Information Exchange for LDPC Iteration Reduction (EEIE-LIR) scheme is introduced to accelerate convergence and reduce receiver complexity. Evaluations based on ATSC 3.0 ModCods demonstrate that the proposed methods significantly improve spectrum efficiency and mitigate the co-channel interferences. The proposed technologies and can be extended to other DTTB systems and cell-based broadband wireless networks (e.g., the fifth generation (5G)/the sixth generation (6G), supporting seamless integration of broadcast, multicast, and unicast services.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"941-953"},"PeriodicalIF":4.8,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Low-Complexity Serial List Viterbi Decoder of CRC-Aided TBCC for Broadcasting Services in 6G","authors":"Dongming Pi;Shancheng Zhao","doi":"10.1109/TBC.2025.3611594","DOIUrl":"https://doi.org/10.1109/TBC.2025.3611594","url":null,"abstract":"In 6G broadcasting, there is an urgent need of a high-performance short-length channel code for hyper-reliable and low-latency communications (HRLLC). Among the available options, cyclic redundancy check-aided tail-biting convolutional code (CRC-TBCC) is recognized as a competitive candidate. To further enhance the competitiveness of CRC-TBCCs, this paper aims to design low-complexity serial list Viterbi decoders (SLVDs) for them. In the proposed decoder, we first apply regular sectionalization on the original trellis to obtain the sectionalized trellis (ST) and then remove all edges whose log-likelihood ratios (LLRs) are below a given threshold to obtain the edge-reduced ST (E-ST). The resulting decoder is termed E-ST-SLVD. We show that there exists an efficient method to determine the desired threshold for ST. We then analyze the decoding complexity the proposed decoder and the interrelationships among key parameters influencing decoding complexity. We finally present extensive numerical results to show that, with appropriate sectionalization length and threshold, the E-ST-SLVD admits a lower complexity than the original SLVD. Meanwhile, we have compared the decoding complexities of several competitive short-length codes for broadcasting to show the advantage of the proposed decoder. These result makes CRC-TBCC an attractive candidate for integrated broadcasting and mobile services in 6G.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"373-381"},"PeriodicalIF":4.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362437","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":"Fusing Learning and Non-Learning: Hybrid CNN-Transformer Cooperative-Competitive Network for Underwater Image Enhancement","authors":"Xun Ji;Xu Wang;Li-Ying Hao;Chengtao Cai;Chengsong Dai;Ryan Wen Liu","doi":"10.1109/TBC.2025.3611669","DOIUrl":"https://doi.org/10.1109/TBC.2025.3611669","url":null,"abstract":"Underwater image enhancement (UIE) aims to provide high-quality observations of challenging underwater scenarios, which is of great significance for various broadcast technologies. Extensive non-learning-based and learning-based UIE methods have been presented and applied. However, non-learning-based strategies typically struggle to demonstrate superior generalization capabilities, while learning-based strategies generally suffer from potential over- or under-enhancement due to the lack of sufficient prior knowledge. To address the challenges above, this paper presents a heuristic cooperative-competitive network, termed Co²Net. Specifically, our Co²Net integrates non-learning mechanisms into the deep learning framework to achieve information fusion from explainable prior knowledge and discernible hierarchical features, thereby facilitating promising and reasonable enhancement of degraded underwater images. Furthermore, our Co²Net adopts a hybrid convolutional neural network (CNN)-Transformer architecture, which comprises successive cooperative-competitive modules (Co²Ms) to achieve adequate extraction, representation, and transmission of both prior knowledge and discernible features. Comprehensive experiments are conducted to demonstrate the superiority and universality of our proposed Co²Net, and sufficient ablation studies are also performed to reveal the effectiveness of each component within our model. The source code is available at <uri>https://github.com/jixun-dmu/Co2Net</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"1065-1078"},"PeriodicalIF":4.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766205","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":"FD-LSCIC: Frequency Decomposition-Based Learned Screen Content Image Compression","authors":"Shiqi Jiang;Hui Yuan;Shuai Li;Huanqiang Zeng;Sam Kwong","doi":"10.1109/TBC.2025.3609052","DOIUrl":"https://doi.org/10.1109/TBC.2025.3609052","url":null,"abstract":"The learned image compression (LIC) methods have already surpassed traditional techniques in compressing natural scene (NS) images. However, directly applying these methods to screen content (SC) images, which possess distinct characteristics such as sharp edges, repetitive patterns, embedded text and graphics, yields suboptimal results. This paper addresses three key challenges in SC image compression: learning compact latent features, adapting quantization step sizes, and the lack of large SC datasets. To overcome these challenges, we propose a novel compression method that employs a multi-frequency two-stage octave residual block (MToRB) for feature extraction, a cascaded triple-scale feature fusion residual block (CTSFRB) for multi-scale feature integration and a multi-frequency context interaction module (MFCIM) to reduce inter-frequency correlations. Additionally, we introduce an adaptive quantization module that learns scaled uniform noise for each frequency component, enabling flexible control over quantization granularity. Furthermore, we construct a large SC image compression dataset (SDU-SCICD10K), which includes over 10,000 images spanning basic SC images, computer-rendered images, and mixed NS and SC images from both PC and mobile platforms. Experimental results demonstrate that our approach significantly improves SC image compression performance, outperforming traditional standards and state-of-the-art learning-based methods in terms of peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM). The source code will be opened later in <uri>https://github.com/SunshineSki/Screen-content-image-dataset/tree/main/SDU-SCICD10K</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"1034-1047"},"PeriodicalIF":4.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766230","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}