IEEE MultiMedia最新文献

{"title":"IEEE Computer Graphics and Applications","authors":"","doi":"10.1109/mmul.2023.3339958","DOIUrl":"https://doi.org/10.1109/mmul.2023.3339958","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"15 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949786","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}

{"title":"IEEE Computer Architecture Letters","authors":"","doi":"10.1109/mmul.2023.3339931","DOIUrl":"https://doi.org/10.1109/mmul.2023.3339931","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"15 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949788","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}

{"title":"Computing in Science & Engineering","authors":"","doi":"10.1109/mmul.2023.3339939","DOIUrl":"https://doi.org/10.1109/mmul.2023.3339939","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"2015 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949888","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}

{"title":"IEEE Computer Society - Call for Papers","authors":"","doi":"10.1109/mmul.2023.3339935","DOIUrl":"https://doi.org/10.1109/mmul.2023.3339935","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"2015 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949780","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}

{"title":"Drive Diversity & Inclusion in Computing","authors":"","doi":"10.1109/mmul.2023.3339930","DOIUrl":"https://doi.org/10.1109/mmul.2023.3339930","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"127 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949778","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}

{"title":"aVCSR: Adaptive Video Compressive Sensing Using Region-of-Interest Detection in the Compressed Domain","authors":"Jian Yang, Haixin Wang, Ittetsu Taniguchi, Yibo Fan, Jinjia Zhou","doi":"10.1109/mmul.2023.3342062","DOIUrl":"https://doi.org/10.1109/mmul.2023.3342062","url":null,"abstract":"Existing video compressive sensing (CS) techniques with fixed sampling rates can deliver satisfactory reconstructed quality but necessitate large transmission bandwidth. To overcome this challenge, region-of-interest (ROI)-based CS algorithms have been introduced to allocate different coding resources between ROI and non-ROI segments. However, neglecting non-ROI excessively in these algorithms leads to unsatisfactory average quality for the eventual reconstruction. In this article, we integrate the ideas of these methods and propose a novel adaptive video CS approach using a low-complexity ROI detection method in the compressed domain. The ROI is detected and sampled by calculating the measurement variance between the reference frame and the subsequent frames. Conversely, the non-ROI is not transmitted but will be reconstructed by utilizing the reference frame through the corresponding position information. In addition, we present a compact method for adapting the threshold value, which allows each frame of a video to have a unique threshold rather than an artificially predetermined fixed value. Moreover, a reference-frame-updating strategy is developed to improve the versatility of the entire framework. Compared to state-of-the-art counterparts, extensive experimental results have demonstrated that our proposed methods achieve superior performance while tackling diverse scenes and using a lower sampling rate.","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140594324","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}

{"title":"Knowledge of Cervical Cancer Screening and Prevention by Human Papillomavirus Deoxyribonucleic Acid and Human Papillomavirus Vaccination among Women Attending a Tertiary Care Centre.","authors":"Sapana Amatya Vaidya, Lisa Roka Magar, Silpina Budha Magar","doi":"10.31729/jnma.8248","DOIUrl":"10.31729/jnma.8248","url":null,"abstract":"<p><strong>Introduction: </strong>Cervical cancer is one of the leading causes of morbidity and mortality among women globally as well as in Nepal. It is attributable to persistent infection by high-risk human papillomavirus, especially human papillomavirus-16 and human papillomavirus-18. The aim of this study was to find out the knowledge of cervical cancer screening and prevention by human papillomavirus deoxyribonucleic acid and human papillomavirus vaccination among women attending a tertiary care centre.</p><p><strong>Methods: </strong>A descriptive cross-sectional study was conducted in patients attending the outpatient Department of Gynaecology in a tertiary care centre from 18 March to 30 April 2023. After calculating sample size and taking a convenience sampling a survey questionnaire on knowledge of Cervical Cancer Screening and Prevention by Human Papillomavirus Deoxyribonucleic Acid and Human Papillomavirus Vaccination was collected. The point estimate was calculated at a 95% confidence interval.</p><p><strong>Results: </strong>Among 508 women, 42 (8.25%) (5.86-10.64, 95% Confidence Interval) had knowledge of cervical cancer screening and prevention by human papillomavirus deoxyribonucleic acid and human papillomavirus vaccination. According to the questionnaires with a total sample of 508, 164 (32.28%) know about cervical cancer, 15 (2.95%) know about HPV infection, 14 (2.76%) know about HPV infection causes cervical cancer, and 21 (4.13%) know about HPV transmitted through multiple sex partners.</p><p><strong>Conclusions: </strong>The knowledge of cervical cancer screening and prevention by human papillomavirus deoxyribonucleic acid and human papillomavirus vaccination among women is very low. This study recommends having a health education and awareness programme on it to increase knowledge.</p><p><strong>Keywords: </strong>cervical cancer; human papillomavirus; pap smear; sexual intercourse; vaccination.</p>","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"1 1","pages":"658-661"},"PeriodicalIF":0.5,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69803539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Edge Distraction-aware Salient Object Detection","authors":"Sucheng Ren, Wenxi Liu, Jianbo Jiao, Guoqiang Han, Shengfeng He","doi":"10.1109/MMUL.2023.3235936","DOIUrl":"https://doi.org/10.1109/MMUL.2023.3235936","url":null,"abstract":"Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Second, we propose to explore cross-scale holistic contextual connections between every position pair of high-level feature maps regardless of their distances across different scales. It selectively aggregates features at each position based on its connections to all the others, simulating the “contrast” stimulus of visual saliency. Finally, we present a complementary features integration module to fuse low- and high-level features according to their properties. Experimental results demonstrate our proposed method outperforms previous state-of-the-art methods on the benchmark datasets, with the fast inference speed of 30 FPS on a single GPU.","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"30 1","pages":"63-73"},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43780891","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}

{"title":"Content-Aware Latent Semantic Direction Fusion for Multi-Attribute Editing","authors":"Xiwen Wei, Yihan Tang, Si Wu","doi":"10.1109/MMUL.2023.3285550","DOIUrl":"https://doi.org/10.1109/MMUL.2023.3285550","url":null,"abstract":"For facial attribute editing, significant progress has been made in discovering semantic directions in the latent space of StyleGAN, and the manipulation is performed by mapping an input image to a latent code and then moving along a direction associated with a target attribute. In this case, multi-attribute editing typically needs a sequential transformation process, which may cause ineffective manipulation or the cumulative effect on irrelevant attribute deviation. In this work, we aim to simultaneously manipulate multiple attributes through a single transformation. Toward this end, we propose a StyleGAN-based latent semantic direction fusion model, referred to as StyleLSF. There are two learnable components: a content-aware direction predictor learns to infer the latent directions, which are associated with preset attributes. A fusion network fuses the directions with respect to target attributes and yields a single translation vector. We further ensure irrelevant attribute preservation by imposing an attribute-aware feature consistency regularization approach.","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"30 1","pages":"87-99"},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42217833","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}

{"title":"Optimizing Multidimensional Perceptual Quality in Online Interactive Multimedia","authors":"Benjamin W. Wah, Jingxi X. Xu","doi":"10.1109/MMUL.2023.3277851","DOIUrl":"https://doi.org/10.1109/MMUL.2023.3277851","url":null,"abstract":"Network latencies and losses in online interactive multimedia applications may lead to a degraded perception of quality, such as lower interactivity or sluggish responses. We can measure these degradations in perceptual quality by the just-noticeable difference, awareness, or probability of noticeability ($p_{text{note}}$pnote); the latter measures the likelihood that subjects can notice a change from a reference to a modified reference. In our previous work, we developed an efficient method for finding the perceptual quality for one metric under simplex control. However, integrating the perceptual qualities of several metrics is a heuristic. In this article, we present a formal approach to optimally combine the perceptual quality of multiple metrics into a joint measure that shows their tradeoffs. Our result shows that the optimal balance occurs when the $p_{text{note}}$pnote of all the component metrics are equal. Furthermore, our approach leads to an algorithm with a linear (instead of combinatorial) complexity of the number of metrics. Finally, we present the application of our method in two case studies, one on VoIP for finding the optimal operating points and the second on fast-action games to hide network delays while maintaining the consistency of action orders.","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"30 1","pages":"119-128"},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47142163","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}