Journal of Visual Communication and Image Representation最新文献

A robust watermarking approach for medical image authentication using dual image and quorum function 使用双图像和法定人数函数的医疗图像认证鲁棒水印方法

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104299

引用次数: 0

Robust text watermarking based on average skeleton mass of characters against cross-media attacks 基于字符平均骨架质量的鲁棒文本水印技术对抗跨媒体攻击

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104300

{"title":"Robust text watermarking based on average skeleton mass of characters against cross-media attacks","authors":"","doi":"10.1016/j.jvcir.2024.104300","DOIUrl":"10.1016/j.jvcir.2024.104300","url":null,"abstract":"<div><div>The wide spread of digital documents makes it essential to protect intellectual property and information security. As a key method of digital copyright protection, robust document watermarking technology has attracted much attention in this context. With the rapid development of current electronic devices, the ways of document theft are no longer limited to copy and transmission. Due to the convenient and fast shooting operation of the camera on paper or screen, current text watermarking methods need to be robust to cope with cross-media transmission. To realize the corresponding robust text watermarking, a text watermarking scheme based on the average skeleton mass of characters is proposed in this paper, and the average skeleton mass of adjacent characters is used to represent the watermark information. In this paper, a watermarking scheme is designed to modify character pixels, which can modify glyphs without loss of transparency and provide high embedding capacity. Compared with the existing manually designed font-based text watermarking schemes, this scheme does not need to accurately segment characters, nor does it rely on stretching characters to the same size for matching, which reduces the need for character segmentation. In addition, the experimental results show that the proposed watermarking scheme can be robust to the information transmission modes including print-scan, print-camera and screen-camera.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective image compression using hybrid DCT and hybrid capsule auto encoder for brain MR images 使用混合 DCT 和混合胶囊自动编码器对脑部 MR 图像进行有效压缩

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104296

引用次数: 0

Diving deep into human action recognition in aerial videos: A survey 深入研究航拍视频中的人类动作识别：调查

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-23 DOI: 10.1016/j.jvcir.2024.104298

引用次数: 0

Zero-CSC: Low-light image enhancement with zero-reference color self-calibration Zero-CSC：利用零参考色彩自校准功能增强低照度图像效果

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-20 DOI: 10.1016/j.jvcir.2024.104293

引用次数: 0

M-YOLOv8s: An improved small target detection algorithm for UAV aerial photography M-YOLOv8s：用于无人机航空摄影的改进型小目标检测算法

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-18 DOI: 10.1016/j.jvcir.2024.104289

{"title":"M-YOLOv8s: An improved small target detection algorithm for UAV aerial photography","authors":"","doi":"10.1016/j.jvcir.2024.104289","DOIUrl":"10.1016/j.jvcir.2024.104289","url":null,"abstract":"<div><div>The object of UAV target detection usually means small target with complicated backgrounds. In this paper, an object detection model M-YOLOv8s based on UAV aerial photography scene is proposed. Firstly, to solve the problem that the YOLOv8s model cannot adapt to small target detection, a small target detection head (STDH) module is introduced to fuse the location and appearance feature information of the shallow layers of the backbone network. Secondly, Inner-Wise intersection over union (Inner-WIoU) is designed as the boundary box regression loss, and auxiliary boundary calculation is used to accelerate the regression speed of the model. Thirdly, the structure of multi-scale feature pyramid network (MS-FPN) can effectively combine the shallow network information with the deep network information and improve the performance of the detection model. Furthermore, a multi-scale cross-spatial attention (MCSA) module is proposed to expand the feature space through multi-scale branch, and then achieves the aggregation of target features through cross-spatial interaction, which improves the ability of the model to extract target features. Finally, the experimental results show that our model does not only possess fewer parameters, but also the values of mAP<sub>0.5</sub> are 6.6% and 5.4% higher than the baseline model on the Visdrone2019 validation dataset and test dataset, respectively. Then, as a conclusion, the M-YOLOv8s model achieves better detection performance than some existing ones, indicating that our proposed method can be more suitable for detecting the small targets.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-complexity content-aware encoding optimization of batch video 批量视频的低复杂度内容感知编码优化

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-17 DOI: 10.1016/j.jvcir.2024.104295

引用次数: 0

Leveraging occupancy map to accelerate video-based point cloud compression 利用占用图加速基于视频的点云压缩

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-16 DOI: 10.1016/j.jvcir.2024.104292

{"title":"Leveraging occupancy map to accelerate video-based point cloud compression","authors":"","doi":"10.1016/j.jvcir.2024.104292","DOIUrl":"10.1016/j.jvcir.2024.104292","url":null,"abstract":"<div><p>Video-based Point Cloud Compression enables point cloud streaming over the internet by converting dynamic 3D point clouds to 2D geometry and attribute videos, which are then compressed using 2D video codecs like H.266/VVC. However, the complex encoding process of H.266/VVC, such as the quadtree with nested multi-type tree (QTMT) partition, greatly hinders the practical application of V-PCC. To address this issue, we propose a fast CU partition method dedicated to V-PCC to accelerate the coding process. Specifically, we classify coding units (CUs) of projected images into three categories based on the occupancy map of a point cloud: unoccupied, partially occupied, and fully occupied. Subsequently, we employ either statistic-based rules or machine-learning models to manage the partition of each category. For unoccupied CUs, we terminate the partition directly; for partially occupied CUs with explicit directions, we selectively skip certain partition candidates; for the remaining CUs (partially occupied CUs with complex directions and fully occupied CUs), we train an edge-driven LightGBM model to predict the partition probability of each partition candidate automatically. Only partitions with high probabilities are retained for further Rate–Distortion (R–D) decisions. Comprehensive experiments demonstrate the superior performance of our proposed method: under the V-PCC common test conditions, our method reduces encoding time by 52% and 44% in geometry and attribute, respectively, while incurring only 0.68% (0.66%) BD-Rate loss in D1 (D2) measurements and 0.79% (luma) BD-Rate loss in attribute, significantly surpassing state-of-the-art works.</p></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SR4KVQA: Video quality assessment database and metric for 4K super-resolution SR4KVQA：用于 4K 超分辨率的视频质量评估数据库和衡量标准

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-14 DOI: 10.1016/j.jvcir.2024.104290

{"title":"SR4KVQA: Video quality assessment database and metric for 4K super-resolution","authors":"","doi":"10.1016/j.jvcir.2024.104290","DOIUrl":"10.1016/j.jvcir.2024.104290","url":null,"abstract":"<div><p>The quality assessment for 4K super-resolution (SR) videos can be conducive to the optimization of video SR algorithms. To improve the subjective and objective consistency of the SR quality assessment, a 4K video database and a blind metric are proposed in this paper. In the database SR4KVQA, there are 30 4K pristine videos, from which 600 SR 4K distorted videos with mean opinion score (MOS) labels are generated by three classic interpolation methods, six SR algorithms based on the deep neural network (DNN), and two SR algorithms based on the generative adversarial network (GAN). The benchmark experiment of the proposed database indicates that video quality assessment (VQA) of the 4K SR videos is challenging for the existing metrics. Among those metrics, the Video-Swin-Transformer backbone demonstrates tremendous potential in the VQA task. Accordingly, a blind VQA metric based on the Video-Swin-Transformer backbone is established, where the normalized loss function and optimized spatio-temporal sampling strategy are applied. The experiment result manifests that the Pearson linear correlation coefficient (PLCC) and Spearman rank-order correlation coefficient (SROCC) of the proposed metric reach 0.8011 and 0.8275 respectively on the SR4KVQA database, which outperforms or competes with the state-of-the-art VQA metrics. The database and the code proposed in this paper are available in the GitHub repository, <span><span>https://github.com/AlexReadyNico/SR4KVQA</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data compensation and feature fusion for sketch based person retrieval 基于素描的人物检索的数据补偿和特征融合

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-09-12 DOI: 10.1016/j.jvcir.2024.104287

引用次数: 0