Pub Date : 2026-03-06DOI: 10.1109/TBC.2026.3666580
{"title":"IEEE Transactions on Broadcasting Information for Readers and Authors","authors":"","doi":"10.1109/TBC.2026.3666580","DOIUrl":"https://doi.org/10.1109/TBC.2026.3666580","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"C3-C4"},"PeriodicalIF":4.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11422825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362542","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}
Pub Date : 2026-02-06DOI: 10.1109/TBC.2026.3659013
Shishun Tian;Qian Xu;He Gui;Ting Su;Yan Li;Qiong Wang
Referring image segmentation (RIS) is a challenging task that requires models to segment objects based on natural language descriptions. Existing RIS models have limited leverage of geometric information, resulting in multimodal mismatch between language and vision. In contrast to conventional 2D images, light field imaging gathers rays emitted from light sources in all directions. This unique characteristic enriches the comprehensive understanding of scenes, which provides us with a new way to optimize RIS. In this paper, we propose the first light field referring segmentation dataset, which contains rich occluded objects and depth-referring descriptions. Afterward, we benchmark the performance of existing 2D referring image segmentation methods on the proposed dataset. The results revealed that these methods show limited efficacy in occluded scenes and depth-based descriptions of scenes. To address this issue, we propose a novel framework, termed LFLLM, for light field referring segmentation. Specifically, we propose a Center Angular Aggregation Module that warps the views adjacent to the central view to prevent feature occlusion caused by viewpoints misalignment, and a Depth Convergence Module that adds a depth token into the LLMs to leverage the depth information in the light field. Extensive experiments demonstrate that our approach outperforms the current state-of-the-art methods. The dataset and code are available at https://github.com/ShishunTian/LFLLM-TBC2026
{"title":"Light Field Referring Segmentation: A Benchmark and an LLM-Based Approach","authors":"Shishun Tian;Qian Xu;He Gui;Ting Su;Yan Li;Qiong Wang","doi":"10.1109/TBC.2026.3659013","DOIUrl":"https://doi.org/10.1109/TBC.2026.3659013","url":null,"abstract":"Referring image segmentation (RIS) is a challenging task that requires models to segment objects based on natural language descriptions. Existing RIS models have limited leverage of geometric information, resulting in multimodal mismatch between language and vision. In contrast to conventional 2D images, light field imaging gathers rays emitted from light sources in all directions. This unique characteristic enriches the comprehensive understanding of scenes, which provides us with a new way to optimize RIS. In this paper, we propose the first light field referring segmentation dataset, which contains rich occluded objects and depth-referring descriptions. Afterward, we benchmark the performance of existing 2D referring image segmentation methods on the proposed dataset. The results revealed that these methods show limited efficacy in occluded scenes and depth-based descriptions of scenes. To address this issue, we propose a novel framework, termed LFLLM, for light field referring segmentation. Specifically, we propose a Center Angular Aggregation Module that warps the views adjacent to the central view to prevent feature occlusion caused by viewpoints misalignment, and a Depth Convergence Module that adds a depth token into the LLMs to leverage the depth information in the light field. Extensive experiments demonstrate that our approach outperforms the current state-of-the-art methods. The dataset and code are available at <uri>https://github.com/ShishunTian/LFLLM-TBC2026</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"361-372"},"PeriodicalIF":4.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362447","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}
Pub Date : 2026-02-06DOI: 10.1109/TBC.2026.3651199
Gosala Kulupana;Jayasingam Adhuran;Andrew Cotton
Along with the ever-growing popularity of information rich video formats such as High Dynamic Range (HDR), Wide Colour Gamut (WCG) and Ultra High Definition (UHD), the need for efficient distribution of such video content has become crucially important. To this end, video compression plays an important role in reducing the bitrate requirements by, for example, up to two orders of magnitude. This work first investigates the performance of state-of-the-art video compression standards with the help of a large-scale subjective study. The study for the first time provides some useful insight into the types of artifacts that are introduced by the video codecs on HDR content with film-grain. The evaluation of the pictures generated by the video codecs is also a crucial aspect in the video distribution pipeline. For this purpose, this paper further provides an HDR picture quality metric which is based on an existing Standard Dynamic Range (SDR) picture quality metric called Detail Loss Metric (DLM). The proposed metric includes several novel features to make it suitable for assessing compression artifacts of HDR content with and without film-grain. They are 1) A Just Noticeable Difference (JND) based perceptual weighting function that captures the non-linearity in the HDR signal. 2) An entropy masking module to capture the impact of film-grain characteristics. 3) A contrast masking operation based on previous work, to better represent the impact of local visual masking. Performance of the proposed method and several state-of-the-art HDR and SDR quality metrics is evaluated on two large-scale datasets using three commonly used accuracy measures: Pearson Linear Correlation Coefficient (PLCC), Spearman’s Rank Correlation Coefficient (SRCC) and Root Mean Square Error (RMSE). A further measure of how reliably a given metric can represent the user perception is also introduced. The experimental results indicate superior performance of the proposed method, demonstrating a PLCC score of more than 94% and similar good performance scores for other accuracy measures.
{"title":"Compression Efficiency and Picture Quality Assessment of Broadcast HDR Videos With and Without Film-Grain","authors":"Gosala Kulupana;Jayasingam Adhuran;Andrew Cotton","doi":"10.1109/TBC.2026.3651199","DOIUrl":"https://doi.org/10.1109/TBC.2026.3651199","url":null,"abstract":"Along with the ever-growing popularity of information rich video formats such as High Dynamic Range (HDR), Wide Colour Gamut (WCG) and Ultra High Definition (UHD), the need for efficient distribution of such video content has become crucially important. To this end, video compression plays an important role in reducing the bitrate requirements by, for example, up to two orders of magnitude. This work first investigates the performance of state-of-the-art video compression standards with the help of a large-scale subjective study. The study for the first time provides some useful insight into the types of artifacts that are introduced by the video codecs on HDR content with film-grain. The evaluation of the pictures generated by the video codecs is also a crucial aspect in the video distribution pipeline. For this purpose, this paper further provides an HDR picture quality metric which is based on an existing Standard Dynamic Range (SDR) picture quality metric called Detail Loss Metric (DLM). The proposed metric includes several novel features to make it suitable for assessing compression artifacts of HDR content with and without film-grain. They are 1) A Just Noticeable Difference (JND) based perceptual weighting function that captures the non-linearity in the HDR signal. 2) An entropy masking module to capture the impact of film-grain characteristics. 3) A contrast masking operation based on previous work, to better represent the impact of local visual masking. Performance of the proposed method and several state-of-the-art HDR and SDR quality metrics is evaluated on two large-scale datasets using three commonly used accuracy measures: Pearson Linear Correlation Coefficient (PLCC), Spearman’s Rank Correlation Coefficient (SRCC) and Root Mean Square Error (RMSE). A further measure of how reliably a given metric can represent the user perception is also introduced. The experimental results indicate superior performance of the proposed method, demonstrating a PLCC score of more than 94% and similar good performance scores for other accuracy measures.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"317-328"},"PeriodicalIF":4.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362477","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}
Pub Date : 2025-12-17DOI: 10.1109/TBC.2025.3640887
{"title":"2025 Scott Helt Memorial Award for the Best Paper Published in IEEE Transactions on Broadcasting","authors":"","doi":"10.1109/TBC.2025.3640887","DOIUrl":"https://doi.org/10.1109/TBC.2025.3640887","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 4","pages":"1108-1110"},"PeriodicalIF":4.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766204","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}
Pub Date : 2025-12-17DOI: 10.1109/TBC.2025.3640761
{"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}
Pub Date : 2025-12-11DOI: 10.1109/TBC.2025.3635396
Kewen Wang;Meng Cheng
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.
{"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}
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 DM-VSR, 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 https://github.com/aigcvsr/DM-VSR
{"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}
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 https://github.com/wasai67/Anime4K
尽管近年来动画超分辨率（SR）引起了越来越多的兴趣，但许多现有的方法仍然继承了来自逼真图像的设计原则，忽视了动画独特的视觉特征。在这项工作中，我们从动画特定的角度重新审视动画SR任务，并提出了为现实世界修复量身定制的统一解决方案。首先，我们提出了A4K，这是第一个专门为动漫SR策划的4k分辨率数据集，通过结合感知质量和结构复杂性的双标准选择管道构建，从而产生更清晰、更有信息量的训练样本。其次，我们引入了AniFusionNet，这是一种CNN-Transformer混合架构，它动态融合了局部卷积特征和全局自关注，有效地平衡了精细细节重建和全局相干性。最后，我们引入了一种有针对性的ground truth （GT）增强策略，该策略可以选择性地增强手绘线结构，从而更准确地学习特定于动画的纹理。在公共基准测试上进行的大量实验表明，我们的方法在边缘清晰度、颜色一致性和伪影抑制方面实现了最先进的性能。该项目可在https://github.com/wasai67/Anime4K上获得
{"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}
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 https://github.com/ZhengyuZhang96/MPT-MWI-BLiF
{"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}
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