2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-MDSP.9
Paper Title LEARNING-BASED LIGHT FIELD VIEW SYNTHESIS FOR EFFICIENT TRANSMISSION AND STORAGE
Authors Abrar Wafa, University of British Columbia, Canada; Mahsa T. Pourazad, TELUS Communications Inc., Canada; Panos Nasiopoulos, University of British Columbia, Canada
SessionMLR-APPL-MDSP: Machine learning for multidimensional signal processing
LocationArea F
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Multidimensional Signal Processing: Applications of multidimensional signal processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract One of the main advantages of Light Field (LF) technology is that it provides a truly immersive experience, critical for computer vision, autonomous driving and medical applications. However, one of the main problems of light field is the size of the data captured, which significantly increases bandwidth requirements. In this paper, we introduce a learning-based LF view synthesis approach for efficient transmission and storage, fundamental for performing remote surgery and storing data. This is achieved by dropping specific views at the transmitting end and then efficiently synthesizing them at the receiver end. Our deep learning approach uses the epipolar image plane (EPI) information to ensure smooth disparity between the generated and original views. We consider plenoptic, synthetic LF content and camera array implementations which support different baseline settings. Experimental results show that our proposed method outperforms state-of-the-art light field view synthesis techniques, offering improved visual quality for the generated views.