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High quality virtual view synthesis for immersive video applications

University of British Columbia

Advances in image and video capturing technologies, coupled with the introduction of innovative Multiview displays, present new opportunities and challenges to content providers and broadcasters. New technologies that allow multiple views to be displayed to the end-user, such as Super Multiview (SMV) and Free Viewpoint Navigation (FN), aim at creating an immersive experience by offering additional degrees of freedom to the user. Since transmission bitrates are proportional to the number of the cameras used, reducing the number of capturing devices and synthesizing/generating intermediate views at the receiver end is necessary for decreasing the required bandwidth and paving the path toward practical implementation. View synthesis is the common approach for creating new virtual views either for expanding the coverage or closing the gap between existing real camera views, depending on the type of Free Viewpoint TV application, i.e., SMV or 2D walk-around-scene-like (FN) immersive experience. In these implementations, it is common for the majority of the cameras to have dissimilar characteristics and different viewpoints often yielding significant luminance and chrominance discrepancies among the captured views. As a result, synthesized views may have visual artifacts, caused by incorrect estimation of missing texture in occluded areas and possible brightness and color differences between the original real views. In this thesis, we propose unique view synthesis methods that address the inefficiencies of conventional view synthesis approaches by eliminating background leakage and using edge-aware background warping and inter-pixel color interpolation techniques to avoid deformation of foreground objects. Improved occlusion filling is achieved by using information from a temporally constructed background. We also propose a new view synthesis method specifically designed for FN applications, addressing the challenge of brightness and color transition between consecutive virtual views. Subjective and objective evaluations showed that our methods significantly improve the overall objective and subjective quality of the synthesized videos.

Text

2018-05-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65733

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/