2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)最新文献_第6页

Icon Colorization Based On Triple Conditional Generative Adversarial Networks 基于三重条件生成对抗网络的图标着色

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301890

Qin-Ru Han, Wenzhe Zhu, Qing Zhu

引用次数: 2

Deep Inter Coding with Interpolated Reference Frame for Hierarchical Coding Structure 基于插值参考帧的层次编码结构深度编码

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301769

Yu Guo, Zizheng Liu, Zhenzhong Chen, Shan Liu

引用次数: 3

Improving Compression Artifact Reduction via End-to-End Learning of Side Information 通过端到端侧信息学习减少压缩伪影

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301805

Haichuan Ma, Dong Liu, Feng Wu

引用次数: 3

An Optimized Video Encoder Implementation with Screen Content Coding Tools 一个优化的视频编码器实现与屏幕内容编码工具

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301875

Xiaozhong Xu, Shitao Wang, Yu Chen, Yiming Li, Qing Zhang, Yushan Zheng, Shan Liu

引用次数: 1

A Mixed Appearance-based and Coding Distortion-based CNN Fusion Approach for In-loop Filtering in Video Coding 基于外观和编码失真的CNN融合视频编码环内滤波方法

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301895

Jian Yue, Yanbo Gao, Shuai Li, Menghu Jia

{"title":"A Mixed Appearance-based and Coding Distortion-based CNN Fusion Approach for In-loop Filtering in Video Coding","authors":"Jian Yue, Yanbo Gao, Shuai Li, Menghu Jia","doi":"10.1109/VCIP49819.2020.9301895","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301895","url":null,"abstract":"With the success of the convolutional neural networks (CNNs) in image denoising and other computer vision tasks, CNNs have been investigated for in-loop filtering in video coding. Many existing methods directly use CNNs as powerful tools for filtering without much analysis on its effect. Considering the in-loop filters process the reconstructed video frames produced from a fixed line of video coding operations, the coding distortion in the reconstructed frames may share similar properties that can be learned by CNNs in addition to being a noisy image. Therefore, in this paper, we first categorize the CNN based filtering into two types of processes: appearance-based CNN filtering and coding distortion-based CNN filtering, and develop a two-stream CNN fusion framework accordingly. In the appearance-based CNN filtering, a CNN processes the reconstructed frame as a distorted image and extracts the global appearance information to restore the original image. In order to extract the global information, a CNN with pooling is used first to increase the receptive field and up-sampling is added in the late stage to produce pixel-level frame information. On the contrary, in the coding distortion-based filtering, a CNN processes the reconstructed frame as blocks with certain types of distortions by focusing on the local information to learn the coding distortion resulted by the fixed video coding pipeline. Finally, the appearance-based filtering stream and the coding distortion-based filtering stream are fused together to combine the two aspects of CNN filtering, and also the global and local information. To further reduce the complexity, the similar initial and last convolutional layers are shared over two streams to generate a mixed CNN. Experiments demonstrate that the proposed method achieves better performance than the existing CNN-based filtering methods, with 11.26% BD-rate saving under the All Intra configuration.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"394 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113997253","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}

引用次数: 3

APL: Adaptive Preloading of Short Video with Lyapunov Optimization 基于Lyapunov优化的短视频自适应预加载

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301886

Haodan Zhang, Yixuan Ban, Xinggong Zhang, Zongming Guo, Zhimin Xu, Shengbin Meng, Junlin Li, Yue Wang

{"title":"APL: Adaptive Preloading of Short Video with Lyapunov Optimization","authors":"Haodan Zhang, Yixuan Ban, Xinggong Zhang, Zongming Guo, Zhimin Xu, Shengbin Meng, Junlin Li, Yue Wang","doi":"10.1109/VCIP49819.2020.9301886","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301886","url":null,"abstract":"Short video applications, like TikTok, have attracted many users across the world. It can feed short videos based on users' preferences and allow users to slide the boring content anywhere and anytime. To reduce the loading time and keep playback smoothness, most of the short video apps will preload the recommended short videos in advance. However, these apps preload short videos in fixed size and fixed order, which can lead to huge playback stall and huge bandwidth waste. To deal with these problems, we present an Adaptive Preloading mechanism for short videos based on Lyapunov Optimization, also called APL, to achieve near-optimal playback experience, i.e., maximizing playback smoothness and minimizing bandwidth waste considering users' sliding behaviors. Specifically, we make three technical contributions: (1) We design a novel short video streaming framework which can dynamically preload the recommended short videos before the current video is downloaded completely. (2) We formulate the preloading problem into a playback experience optimization problem to maximize the playback smoothness and minimize the bandwidth waste. (3) We transform the playback experience optimization problem during the whole viewing process into a single-step greedy algorithm based on the Lyapunov optimization theory to make the online decisions during playback. Through extensive experiments based on the real datasets that generously provided by TikTok, we demonstrate that APL can reduce the stall ratio by 81%/12% and bandwidth waste by 11%/31% compared with no-preloading/fixed-preloading mechanism.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114011376","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}

引用次数: 10

A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy 一种具有可调隐私的自然人脸去识别混合模型

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301866

Yunqian Wen, Bo Liu, Rong Xie, Yunhui Zhu, Jingyi Cao, Li Song

{"title":"A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy","authors":"Yunqian Wen, Bo Liu, Rong Xie, Yunhui Zhu, Jingyi Cao, Li Song","doi":"10.1109/VCIP49819.2020.9301866","DOIUrl":"https://doi.org/10.1109/VCIP49819.2020.9301866","url":null,"abstract":"As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, becomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing de-identified face images according to a single input. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing de-identified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.","PeriodicalId":431880,"journal":{"name":"2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944552","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}

引用次数: 5

Quality of Experience Evaluation for Streaming Video Using CGNN 基于CGNN的流媒体视频体验质量评价

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301799

Zhiming Zhou, Yu Dong, Li Song, Rong Xie, Lin Li, Bing Zhou

引用次数: 3

Application of Brain-Computer Interface and Virtual Reality in Advancing Cultural Experience 脑机接口与虚拟现实在提升文化体验中的应用

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301801

Hao-Lun Fu, Po-Hsiang Fang, Chan-Yu Chi, Chung-ting Kuo, Meng-Hsuan Liu, Howard Muchen Hsu, Cheng-Hsun Hsieh, Sheng-Fu Liang, S. Hsieh, Cheng-Ta Yang

引用次数: 3

The Hough-Based Multibeamlet Transform 基于霍夫的多波束变换

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) Pub Date : 2020-12-01 DOI: 10.1109/VCIP49819.2020.9301812

A. Lisowska

引用次数: 0