Pub Date : 2024-09-16DOI: 10.1109/TBC.2024.3434731
Shuai Han;Zhiqiang Li;Weixiao Meng;Cheng Li
The multibeam satellite system is crucial for providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within a limited spectrum of resources presents formidable challenges. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using rate-splitting multiple access (RSMA), which leverages non-orthogonal transmission and precoding strategies. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose two novel distributed frequency reuse (DFR) and centralized frequency reuse (CFR) strategies. Taking satellite beam gain characteristics and interference tolerance threshold into consideration, we further propose another two expansions of DFR and CFR strategies with innovative inner and outer divisions. For the NOBU joint transmission based on four novel frequency reuse patterns, we maximize the weighted sum rate (WSR). Subsequently, we introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problems into tractable convex counterparts. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.
{"title":"Next-Gen Satellite System: Integrative Non-Orthogonal Broadcast and Unicast Services Based on Innovative Frequency Reuse Patterns","authors":"Shuai Han;Zhiqiang Li;Weixiao Meng;Cheng Li","doi":"10.1109/TBC.2024.3434731","DOIUrl":"10.1109/TBC.2024.3434731","url":null,"abstract":"The multibeam satellite system is crucial for providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within a limited spectrum of resources presents formidable challenges. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using rate-splitting multiple access (RSMA), which leverages non-orthogonal transmission and precoding strategies. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose two novel distributed frequency reuse (DFR) and centralized frequency reuse (CFR) strategies. Taking satellite beam gain characteristics and interference tolerance threshold into consideration, we further propose another two expansions of DFR and CFR strategies with innovative inner and outer divisions. For the NOBU joint transmission based on four novel frequency reuse patterns, we maximize the weighted sum rate (WSR). Subsequently, we introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problems into tractable convex counterparts. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1153-1166"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263748","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 : 2024-09-16DOI: 10.1109/TBC.2024.3453611
{"title":"IEEE Transactions on Broadcasting Information for Authors","authors":"","doi":"10.1109/TBC.2024.3453611","DOIUrl":"https://doi.org/10.1109/TBC.2024.3453611","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"C3-C4"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235712","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}
Existing no reference image quality assessment(NR-IQA) methods have not incorporated image semantics explicitly in the assessment process, thus overlooking the significant correlation between image content and its quality. To address this gap, we leverages image semantics as guiding information for quality assessment, integrating it explicitly into the NR-IQA process through a Semantic-Guided NR-IQA model(SGIQA), which is based on the Swin Transformer. Specifically, we introduce a Semantic Attention Module and a Perceptual Rule Learning Module. The Semantic Attention Module refines the features extracted by the deep network according to the image content, allowing the network to dynamically extract quality perceptual features according to the semantic context of the image. The Perceptual Rule Learning Module generates parameters for the image quality regression module tailored to the image content, facilitating a dynamic assessment of image quality based on its semantic information. Employing the Swin Transformer and integrating these two modules, we have developed the final semantic-guided NR-IQA model. Extensive experiments on five widely-used IQA datasets demonstrate that our method not only exhibits excellent generalization capabilities but also achieves state-of-the-art performance.
{"title":"SGIQA: Semantic-Guided No-Reference Image Quality Assessment","authors":"Linpeng Pan;Xiaozhe Zhang;Fengying Xie;Haopeng Zhang;Yushan Zheng","doi":"10.1109/TBC.2024.3450320","DOIUrl":"10.1109/TBC.2024.3450320","url":null,"abstract":"Existing no reference image quality assessment(NR-IQA) methods have not incorporated image semantics explicitly in the assessment process, thus overlooking the significant correlation between image content and its quality. To address this gap, we leverages image semantics as guiding information for quality assessment, integrating it explicitly into the NR-IQA process through a Semantic-Guided NR-IQA model(SGIQA), which is based on the Swin Transformer. Specifically, we introduce a Semantic Attention Module and a Perceptual Rule Learning Module. The Semantic Attention Module refines the features extracted by the deep network according to the image content, allowing the network to dynamically extract quality perceptual features according to the semantic context of the image. The Perceptual Rule Learning Module generates parameters for the image quality regression module tailored to the image content, facilitating a dynamic assessment of image quality based on its semantic information. Employing the Swin Transformer and integrating these two modules, we have developed the final semantic-guided NR-IQA model. Extensive experiments on five widely-used IQA datasets demonstrate that our method not only exhibits excellent generalization capabilities but also achieves state-of-the-art performance.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1292-1301"},"PeriodicalIF":3.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207648","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 : 2024-09-06DOI: 10.1109/TBC.2024.3443466
Meixia Hu;Jingqing Wang;Wenchi Cheng;Hailin Zhang
Since the inherent high envelope fluctuation characteristics of OFDM signals present a significant challenge in reducing energy consumption, it is crucial to minimize the range of the envelope fluctuations of OFDM signals. As companding is a well-known technique for reducing the envelope fluctuations of OFDM signals, we explore the optimal companding transform by building a multi-objective optimization model with the goal of minimizing peak-to-average power ratio (PAPR), inner-band distortions, and out-of-band (OOB) radiations in this paper. The solution reveals that the optimal form of companding transform is a piecewise one and closely resembles a linear transform. Furthermore, we find that the average power of the optimal companded signal is never greater than that of the original signal, which contradicts the constraint of constant average signal power usually used in the design of companding transform. Based on the characteristics of the optimal companding transform, we propose a near-optimal piecewise linear companding transform to obviate the extremely high computational complexity of the optimal companding transform. The proposed near-optimal piecewise linear companding transform is a promising solution for mitigating companding distortions while reducing PAPR. However, it should be noted that there may still be some unavoidable distortions after decompanding, which results in a degradation of the BER performance. Thus, we diminish the remaining distortions after decompanding by relaxing the constraint of the proposed near-optimal piecewise linear companding transform on the average power of the companded signals. Simulation results demonstrate that the relaxation can improve the BER performance while ensuring the PAPR performance with only a small sacrifice on OOB radiations.
{"title":"Near-Optimal Piecewise Linear Companding Transform for PAPR Reduction of OFDM Systems","authors":"Meixia Hu;Jingqing Wang;Wenchi Cheng;Hailin Zhang","doi":"10.1109/TBC.2024.3443466","DOIUrl":"10.1109/TBC.2024.3443466","url":null,"abstract":"Since the inherent high envelope fluctuation characteristics of OFDM signals present a significant challenge in reducing energy consumption, it is crucial to minimize the range of the envelope fluctuations of OFDM signals. As companding is a well-known technique for reducing the envelope fluctuations of OFDM signals, we explore the optimal companding transform by building a multi-objective optimization model with the goal of minimizing peak-to-average power ratio (PAPR), inner-band distortions, and out-of-band (OOB) radiations in this paper. The solution reveals that the optimal form of companding transform is a piecewise one and closely resembles a linear transform. Furthermore, we find that the average power of the optimal companded signal is never greater than that of the original signal, which contradicts the constraint of constant average signal power usually used in the design of companding transform. Based on the characteristics of the optimal companding transform, we propose a near-optimal piecewise linear companding transform to obviate the extremely high computational complexity of the optimal companding transform. The proposed near-optimal piecewise linear companding transform is a promising solution for mitigating companding distortions while reducing PAPR. However, it should be noted that there may still be some unavoidable distortions after decompanding, which results in a degradation of the BER performance. Thus, we diminish the remaining distortions after decompanding by relaxing the constraint of the proposed near-optimal piecewise linear companding transform on the average power of the companded signals. Simulation results demonstrate that the relaxation can improve the BER performance while ensuring the PAPR performance with only a small sacrifice on OOB radiations.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"350-359"},"PeriodicalIF":3.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207651","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}
Learning-based point cloud compression has achieved great success in Rate-Distortion (RD) efficiency. Existing methods usually utilize Variational AutoEncoder (VAE) network, which might lead to poor detail reconstruction and high computational complexity. To address these issues, we propose a Scale-adaptive Asymmetric Sparse Variational AutoEncoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which exploits multi-resolution branches to aggregate multiscale features at encoder, and excludes symmetric feature fusion branches to control the model complexity at decoder. Second, we design a Scale Adaptive Feature Refinement Structure (SAFRS) to adaptively adjust the number of Feature Refinement Modules (FRMs), thereby improving RD performance with an acceptable computational overhead. Third, we implement our framework with AMSC and SAFRS, and train it with an RD loss based on Fine-grained Weighted Binary Cross-Entropy (FWBCE) function. Experimental results on 8iVFB, Owlii, and MVUV datasets show that our method outperforms several popular methods, with a 90.0% time reduction and a 51.8% BD-BR saving compared with V-PCC. The code will be available soon at https://github.com/fancj2017/SAS-VAE .
{"title":"Scale-Adaptive Asymmetric Sparse Variational AutoEncoder for Point Cloud Compression","authors":"Jian Chen;Yingtao Zhu;Wei Huang;Chengdong Lan;Tiesong Zhao","doi":"10.1109/TBC.2024.3437161","DOIUrl":"10.1109/TBC.2024.3437161","url":null,"abstract":"Learning-based point cloud compression has achieved great success in Rate-Distortion (RD) efficiency. Existing methods usually utilize Variational AutoEncoder (VAE) network, which might lead to poor detail reconstruction and high computational complexity. To address these issues, we propose a Scale-adaptive Asymmetric Sparse Variational AutoEncoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which exploits multi-resolution branches to aggregate multiscale features at encoder, and excludes symmetric feature fusion branches to control the model complexity at decoder. Second, we design a Scale Adaptive Feature Refinement Structure (SAFRS) to adaptively adjust the number of Feature Refinement Modules (FRMs), thereby improving RD performance with an acceptable computational overhead. Third, we implement our framework with AMSC and SAFRS, and train it with an RD loss based on Fine-grained Weighted Binary Cross-Entropy (FWBCE) function. Experimental results on 8iVFB, Owlii, and MVUV datasets show that our method outperforms several popular methods, with a 90.0% time reduction and a 51.8% BD-BR saving compared with V-PCC. The code will be available soon at \u0000<uri>https://github.com/fancj2017/SAS-VAE</uri>\u0000.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"884-894"},"PeriodicalIF":3.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207649","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}
Face retouching involves using digital techniques to alter an individual’s appearance, commonly using in social media. However, excessively retouched face (RF) images can lead to issues such as unrealistic beauty standards and psychological stress. Therefore, it is crucial to develop a reliable quality assessment method for RF images. In this paper, we propose a novel network named DIRF-IQA for RF image quality assessment (IQA). DIRF-IQA mainly includes a parameter-shared image encoder, a text encoder, and three key components, namely the Differential Feature Attention Module (DFAM), the Text-image Interaction Module (TIM), and the Multi-scale Feature Fusion Module (MFFM). Specifically, the DFAM captures both local and global differences between original and retouched images by processing multi-scale features and utilizing cross-attention and self-attention blocks for differential perception. In the TIM, textual prompts summarizing retouching operations are encoded by a text encoder and integrated with differential features extracted by the DFAM to enhance the understanding of distortions in RF images. The MFFM then fuses these text-enhanced features across different layers and combines them with the global differential feature to predict the quality of the retouched images. We conduct extensive experiments on two RF IQA databases and the results demonstrate the superiority of DIDF-IQA compared to 12 state-of-the-art full-reference IQA methods in evaluating RF images.
{"title":"Retouched Face Image Quality Assessment Based on Differential Perception and Textual Prompt","authors":"Tianwei Zhou;Songbai Tan;Gang Li;Shishun Tian;Chang Tang;Zhihua Wang;Guanghui Yue","doi":"10.1109/TBC.2024.3447454","DOIUrl":"10.1109/TBC.2024.3447454","url":null,"abstract":"Face retouching involves using digital techniques to alter an individual’s appearance, commonly using in social media. However, excessively retouched face (RF) images can lead to issues such as unrealistic beauty standards and psychological stress. Therefore, it is crucial to develop a reliable quality assessment method for RF images. In this paper, we propose a novel network named DIRF-IQA for RF image quality assessment (IQA). DIRF-IQA mainly includes a parameter-shared image encoder, a text encoder, and three key components, namely the Differential Feature Attention Module (DFAM), the Text-image Interaction Module (TIM), and the Multi-scale Feature Fusion Module (MFFM). Specifically, the DFAM captures both local and global differences between original and retouched images by processing multi-scale features and utilizing cross-attention and self-attention blocks for differential perception. In the TIM, textual prompts summarizing retouching operations are encoded by a text encoder and integrated with differential features extracted by the DFAM to enhance the understanding of distortions in RF images. The MFFM then fuses these text-enhanced features across different layers and combines them with the global differential feature to predict the quality of the retouched images. We conduct extensive experiments on two RF IQA databases and the results demonstrate the superiority of DIDF-IQA compared to 12 state-of-the-art full-reference IQA methods in evaluating RF images.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"240-251"},"PeriodicalIF":3.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207650","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}
In recent years, numerous learned video compression (LVC) methods have emerged, demonstrating rapid developments and satisfactory performance. However, in most previous methods, only the previous one frame is used as reference. Although some works introduce the usage of the previous multiple frames, the exploitation of temporal information is not comprehensive. Our proposed method not only utilizes the short-term information from multiple neighboring frames but also introduces long-term feature information as the reference, which effectively enhances the quality of the context and improves the compression efficiency. In our scheme, we propose the long-term information exploitation mechanism to capture long-term temporal relevance. The update and propagation of long-term information establish an implicit connection between the latent representation of the current frame and distant reference frames, aiding in the generation of long-term context. Meanwhile, the short-term neighboring frames are also utilized to extract local information and generate short-term context. The fusion of long-term context and short-term context results in a more comprehensive and high-quality context to achieve sufficient temporal information mining. Besides, the multiple frames information also helps to improve the efficiency of motion compression. They are used to generate the predicted motion and remove spatio-temporal redundancies in motion information by second-order motion prediction and fusion. Experimental results demonstrate that our proposed efficient learned video compression scheme can achieve 4.79% BD-rate saving when compared with H.266 (VTM).
{"title":"Long-Term and Short-Term Information Propagation and Fusion for Learned Video Compression","authors":"Shen Wang;Donghui Feng;Guo Lu;Zhengxue Cheng;Li Song;Wenjun Zhang","doi":"10.1109/TBC.2024.3434702","DOIUrl":"10.1109/TBC.2024.3434702","url":null,"abstract":"In recent years, numerous learned video compression (LVC) methods have emerged, demonstrating rapid developments and satisfactory performance. However, in most previous methods, only the previous one frame is used as reference. Although some works introduce the usage of the previous multiple frames, the exploitation of temporal information is not comprehensive. Our proposed method not only utilizes the short-term information from multiple neighboring frames but also introduces long-term feature information as the reference, which effectively enhances the quality of the context and improves the compression efficiency. In our scheme, we propose the long-term information exploitation mechanism to capture long-term temporal relevance. The update and propagation of long-term information establish an implicit connection between the latent representation of the current frame and distant reference frames, aiding in the generation of long-term context. Meanwhile, the short-term neighboring frames are also utilized to extract local information and generate short-term context. The fusion of long-term context and short-term context results in a more comprehensive and high-quality context to achieve sufficient temporal information mining. Besides, the multiple frames information also helps to improve the efficiency of motion compression. They are used to generate the predicted motion and remove spatio-temporal redundancies in motion information by second-order motion prediction and fusion. Experimental results demonstrate that our proposed efficient learned video compression scheme can achieve 4.79% BD-rate saving when compared with H.266 (VTM).","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1254-1265"},"PeriodicalIF":3.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207652","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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