Real-Time Light Field Video Focusing and GPU Accelerated Streaming

被引:3
|
作者
Chlubna, Tomas [1 ]
Milet, Tomas [1 ]
Zemcik, Pavel [1 ]
Kula, Michal [1 ]
机构
[1] Brno Univ Technol, Fac Informat Technol, Dept Comp Graph & Multimedia, Bozetechova 2, Brno 61200, Czech Republic
关键词
Light field; GPU; Image-based rendering; DEPTH; RECONSTRUCTION;
D O I
10.1007/s11265-023-01874-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a novel solution of real-time depth range and correct focusing estimation in light field videos represented by arrays of video sequences. This solution, compared to previous approaches, offers a novel way to render high-quality synthetic views from light field videos on contemporary hardware in real-time. Only the video frames containing color information with no other attributes, such as captured depth, are needed. The drawbacks of the previous proposals such as block artifacts in the defocused parts of the scene or manual setting of the depth range are also solved in this paper. This paper describes a complete solution that solves the main memory and performance issues of light field rendering on contemporary personal computers. The whole integration of high-quality light field videos supersedes the approaches in previous works and the paper also provides measurements and experimental results. While reaching the same quality as slower state-of-the-art approaches, this method can still be used in real-time which makes it suitable for industry and real-life scenarios as an alternative to standard 3D rendering approaches.
引用
收藏
页码:703 / 719
页数:17
相关论文
共 50 条
  • [41] Real-time video photomosaics with optimized image set and GPU
    Choi, Yoon-Seok
    Jung, Soonchul
    Kim, Jae Woo
    Koo, Bon-Ki
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2014, 9 (03) : 569 - 578
  • [42] A GPU-Enabled Framework for Light Field Efficient Compression and Real-Time Rendering
    Zhao, Mingyuan
    Sheng, Hao
    Chen, Rongshan
    Cong, Ruixuan
    Wang, Tun
    Cui, Zhenglong
    Yang, Da
    Wang, Shuai
    Ke, Wei
    IEEE TRANSACTIONS ON COMPUTERS, 2025, 74 (04) : 1168 - 1181
  • [43] Real-time Panorama Composition for Video Surveillance using GPU
    Shete, Pritam Prakash
    Sarode, Dinesh Madhukar
    Bose, Surojit Kumar
    2016 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2016, : 137 - 143
  • [44] Real-Time GPU Based Video Segmentation with Depth Information
    Bidyanta, Nilangshu
    Akoglu, Ali
    2018 IEEE/ACS 15TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2018,
  • [45] HFR Projector Camera Based Visible Light Communication System for Real-Time Video Streaming
    Sharma, Atul
    Raut, Sushil
    Shimasaki, Kohei
    Senoo, Taku
    Ishii, Idaku
    SENSORS, 2020, 20 (18) : 1 - 28
  • [46] Dynamically adapted streaming of video for a real-time multimedia application
    Thakur, A
    Lines, B
    Reynolds, P
    2005 Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services (ICAS/ICNS), 2005, : 309 - 314
  • [47] REAL-TIME VIDEO STREAMING WITH INTERACTIVE REGION-OF-INTEREST
    Makar, Mina
    Mavlankar, Aditya
    Agrawal, Piyush
    Girod, Bernd
    2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 4437 - 4440
  • [48] A Real-Time Remote Video Streaming Platform for Ultrasound Imaging
    Ahmadi, Mehdi
    Gross, Warren J.
    Kadoury, Samuel
    2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2016, : 4383 - 4386
  • [49] Real-time system for adaptive video streaming based on SVC
    Wien, Mathias
    Cazoulat, Renaud
    Graffunder, Andreas
    Hutter, Andreas
    Amon, Peter
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2007, 17 (09) : 1227 - 1237
  • [50] Real-time decompression of streaming video using mobile code
    Grama, A
    Meyer, D
    Szpankowski, W
    DCC 2001: DATA COMPRESSION CONFERENCE, PROCEEDINGS, 2001, : 496 - 496