Pub Date : 2026-01-27DOI: 10.1109/TCSVT.2025.3647242
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TCSVT.2025.3647242","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3647242","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 1","pages":"C3-C3"},"PeriodicalIF":11.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11365555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/TCSVT.2026.3652903
{"title":"2025 Index IEEE Transactions on Circuits and Systems for Video Technology","authors":"","doi":"10.1109/TCSVT.2026.3652903","DOIUrl":"https://doi.org/10.1109/TCSVT.2026.3652903","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 12","pages":"12925-13126"},"PeriodicalIF":11.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11352539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1109/TCSVT.2025.3634931
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TCSVT.2025.3634931","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3634931","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 12","pages":"C3-C3"},"PeriodicalIF":11.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1109/TCSVT.2025.3623686
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TCSVT.2025.3623686","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3623686","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 11","pages":"C3-C3"},"PeriodicalIF":11.1,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11223417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-03DOI: 10.1109/TCSVT.2025.3612531
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TCSVT.2025.3612531","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3612531","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 10","pages":"C3-C3"},"PeriodicalIF":11.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11192813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1109/TCSVT.2025.3600974
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TCSVT.2025.3600974","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3600974","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 9","pages":"C3-C3"},"PeriodicalIF":11.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11154653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1109/TCSVT.2025.3600972
{"title":"IEEE Transactions on Circuits and Systems for Video Technology Publication Information","authors":"","doi":"10.1109/TCSVT.2025.3600972","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3600972","url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"35 9","pages":"C2-C2"},"PeriodicalIF":11.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11154656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The popularity of template-generated videos has recently experienced a significant increase on social media platforms. In general, videos from the same template share similar temporal characteristics, which are unfortunately ignored in the current compression schemes. In view of this, we aim to examine how such temporal priors from templates can be effectively utilized during the compression process for template-generated videos. First, a comprehensive statistical analysis is conducted, revealing that the coding decisions, including the merge, non-affine, and motion information, across template-generated videos are strongly correlated. Subsequently, leveraging such correlations as prior knowledge, a simple yet effective prior-driven compression scheme for template-generated videos is proposed. In particular, a mode decision pruning algorithm is devised to dynamically skip unnecessarily advanced motion vector prediction (AMVP) or affine AMVP decisions. Moreover, an improved AMVP motion estimation algorithm is applied to further accelerate reference frame selection and the motion estimation process. Experimental results on the versatile video coding (VVC) platform VTM-23.0 demonstrate that the proposed scheme achieves moderate time reductions of 14.31% and 14.99% under the Low-Delay P (LDP) and Low-Delay B (LDB) configurations, respectively, while maintaining negligible increases in Bjøntegaard Delta Rate (BD-Rate) of 0.15% and 0.18%, respectively.
{"title":"Mining Temporal Priors for Template-Generated Video Compression","authors":"Feng Xing;Yingwen Zhang;Meng Wang;Hengyu Man;Yongbing Zhang;Shiqi Wang;Xiaopeng Fan;Wen Gao","doi":"10.1109/TCSVT.2025.3599239","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3599239","url":null,"abstract":"The popularity of template-generated videos has recently experienced a significant increase on social media platforms. In general, videos from the same template share similar temporal characteristics, which are unfortunately ignored in the current compression schemes. In view of this, we aim to examine how such temporal priors from templates can be effectively utilized during the compression process for template-generated videos. First, a comprehensive statistical analysis is conducted, revealing that the coding decisions, including the merge, non-affine, and motion information, across template-generated videos are strongly correlated. Subsequently, leveraging such correlations as prior knowledge, a simple yet effective prior-driven compression scheme for template-generated videos is proposed. In particular, a mode decision pruning algorithm is devised to dynamically skip unnecessarily advanced motion vector prediction (AMVP) or affine AMVP decisions. Moreover, an improved AMVP motion estimation algorithm is applied to further accelerate reference frame selection and the motion estimation process. Experimental results on the versatile video coding (VVC) platform VTM-23.0 demonstrate that the proposed scheme achieves moderate time reductions of 14.31% and 14.99% under the Low-Delay P (LDP) and Low-Delay B (LDB) configurations, respectively, while maintaining negligible increases in Bjøntegaard Delta Rate (BD-Rate) of 0.15% and 0.18%, respectively.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 1","pages":"1160-1172"},"PeriodicalIF":11.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing image steganography schemes always introduce obvious modification traces to the cover image, resulting in the risk of secret information leakage. To address this issue, an end-to-end framework for joint makeup style transfer and image steganography is proposed in this paper to achieve imperceptible higher-capacity data hiding. In the scheme, a Parsing-guided Semantic Feature Alignment (PSFA) module is designed to transfer the style of a makeup image to an object non-makeup image, thereby generating a content-style integrated feature matrix. Meanwhile, a Multi-Scale Feature Fusion and Data Embedding (MFFDE) module was devised to encode the secret image into its latent features and fuse them with the generated content-style integrated feature matrix, as well as the non-makeup image features across multiple scales, to achieve the makeup-stego image. As a result, the style of the makeup image is well transformed and the secret image is imperceptibly embedded simultaneously without directly modifying the pixels of the original non-makeup image. Additionally, a Residual-aware Information Compensation Network (RICN) is developed to compensate the loss of the secret image arising from the multilevel data embedding, thereby further enhancing the quality of the reconstructed secret image. Experimental results show that the proposed scheme achieves superior steganalysis resistance capability and visual quality in both makeup-stego images and recovered secret images, compared with other state-of-the-art schemes.
{"title":"An End-to-End Framework for Joint Makeup Style Transfer and Image Steganography","authors":"Meihong Yang;Ziyi Feng;Bin Ma;Jian Xu;Yongjin Xian;Linna Zhou","doi":"10.1109/TCSVT.2025.3599551","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3599551","url":null,"abstract":"Existing image steganography schemes always introduce obvious modification traces to the cover image, resulting in the risk of secret information leakage. To address this issue, an end-to-end framework for joint makeup style transfer and image steganography is proposed in this paper to achieve imperceptible higher-capacity data hiding. In the scheme, a Parsing-guided Semantic Feature Alignment (PSFA) module is designed to transfer the style of a makeup image to an object non-makeup image, thereby generating a content-style integrated feature matrix. Meanwhile, a Multi-Scale Feature Fusion and Data Embedding (MFFDE) module was devised to encode the secret image into its latent features and fuse them with the generated content-style integrated feature matrix, as well as the non-makeup image features across multiple scales, to achieve the makeup-stego image. As a result, the style of the makeup image is well transformed and the secret image is imperceptibly embedded simultaneously without directly modifying the pixels of the original non-makeup image. Additionally, a Residual-aware Information Compensation Network (RICN) is developed to compensate the loss of the secret image arising from the multilevel data embedding, thereby further enhancing the quality of the reconstructed secret image. Experimental results show that the proposed scheme achieves superior steganalysis resistance capability and visual quality in both makeup-stego images and recovered secret images, compared with other state-of-the-art schemes.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 1","pages":"1293-1308"},"PeriodicalIF":11.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generating high-quality facial photos from fine-detailed sketches is a long-standing research topic that remains unsolved. The scarcity of large-scale paired data due to the cost of acquiring hand-drawn sketches poses a major challenge. Existing methods either lose identity information with oversimplified representations, or rely on costly inversion and strict alignment when using StyleGAN-based priors, limiting their practical applicability. Our primary finding in this work is that the discrete codebook and decoder trained through self-reconstruction in the photo domain can learn rich priors, helping to reduce ambiguity in cross-domain mapping even with current small-scale paired datasets. Based on this, a cross-domain mapping network can be directly constructed. However, empirical findings indicate that using the discrete codebook for cross-domain mapping often results in unrealistic textures and distorted spatial layouts. Therefore, we propose a Hierarchical Adaptive Texture-Spatial Correction (HATSC) module to correct the flaws in texture and spatial layouts. Besides, we introduce a Saliency-based Key Details Enhancement (SKDE) module to further enhance the synthesis quality. Overall, we present a “reconstruct-cross-enhance” pipeline for synthesizing facial photos from fine-detailed sketches. Experiments demonstrate that our method generates high-quality facial photos and significantly outperforms previous approaches across a wide range of challenging benchmarks. The code is publicly available at: https://github.com/Gardenia-chen/DECP
{"title":"Fine-Detailed Facial Sketch-to-Photo Synthesis With Detail-Enhanced Codebook Priors","authors":"Mingrui Zhu;Jianhang Chen;Xin Wei;Nannan Wang;Xinbo Gao","doi":"10.1109/TCSVT.2025.3598016","DOIUrl":"https://doi.org/10.1109/TCSVT.2025.3598016","url":null,"abstract":"Generating high-quality facial photos from fine-detailed sketches is a long-standing research topic that remains unsolved. The scarcity of large-scale paired data due to the cost of acquiring hand-drawn sketches poses a major challenge. Existing methods either lose identity information with oversimplified representations, or rely on costly inversion and strict alignment when using StyleGAN-based priors, limiting their practical applicability. Our primary finding in this work is that the discrete codebook and decoder trained through self-reconstruction in the photo domain can learn rich priors, helping to reduce ambiguity in cross-domain mapping even with current small-scale paired datasets. Based on this, a cross-domain mapping network can be directly constructed. However, empirical findings indicate that using the discrete codebook for cross-domain mapping often results in unrealistic textures and distorted spatial layouts. Therefore, we propose a Hierarchical Adaptive Texture-Spatial Correction (HATSC) module to correct the flaws in texture and spatial layouts. Besides, we introduce a Saliency-based Key Details Enhancement (SKDE) module to further enhance the synthesis quality. Overall, we present a “reconstruct-cross-enhance” pipeline for synthesizing facial photos from fine-detailed sketches. Experiments demonstrate that our method generates high-quality facial photos and significantly outperforms previous approaches across a wide range of challenging benchmarks. The code is publicly available at: <uri>https://github.com/Gardenia-chen/DECP</uri>","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"36 1","pages":"1075-1088"},"PeriodicalIF":11.1,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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