{"title":"3DoF+ video compression algorithm based on distortion minimization interpolation with adaptive reference view selection","authors":"Mingliu Sun, Shengyang Zhao, Xiaoming Chen","doi":"10.1145/3381271.3381279","DOIUrl":"https://doi.org/10.1145/3381271.3381279","url":null,"abstract":"The emerging 3DoF+ video offers more interactivity and deeper immersion. However, the bulky size of the 3DoF+ video imposes new challenges in its storage and processing. Thus, compressing 3DoF+ video efficiently is vitally important. Due to the geometric relationship between viewpoints, overlaps exist in views and this redundancy is available for further compression. However, existing compression algorithms either utilize partial information of the scene or still retain a lot of redundancy, which results in low quality reconstructed views. To address these problems, our study proposes to select reference views adaptively, which utilizes as few bits as possible to contain the necessary visual information of the scene. Besides, in order to reduce the residues between the reconstructed and original views, the distortion minimization interpolation (DMI) method is then proposed for further refining the synthesized views. The experimental results show that our method achieves on average 52.1% BD-rate reduction than direct coding with High Efficiency Video Coding (HEVC).","PeriodicalId":124651,"journal":{"name":"Proceedings of the 5th International Conference on Multimedia and Image Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Two-stage metric learning for cross-modality person re-identification","authors":"Jiabao Wang, Shanshan Jiao, Yang Li, Zhuang Miao","doi":"10.1145/3381271.3381285","DOIUrl":"https://doi.org/10.1145/3381271.3381285","url":null,"abstract":"Cross-modality person re-identification faces big challenges as the different characteristics of images collected by visible and thermal cameras. The existing deep learning methods always use metric learning to learn the discriminative features. However, the existing metric learning is executed based on batch examples, the solution is local optimal. In order to learn a global solution, we propose a two-stage metric learning (TML) method, which uses local and global metric learning successively. In the first stage, a local metric learning is used based on mini-batch images via triplet loss. A new mixed-modality triplet loss is proposed to train more valid triplet examples. It supervises to learn more efficient features for the next stage. In the second stage, a global metric learning is adopted based on the features of all training images. Experiments are conducted on the public SYSU-MM01 dataset. The TML achieved 39.75% in Rank-1 and 42.73% in mAP, which surpass the state-of-the-art performance.","PeriodicalId":124651,"journal":{"name":"Proceedings of the 5th International Conference on Multimedia and Image Processing","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116016169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}