Abstract
Virtual reality (VR) has been one of the most important topics in the field of multimedia signals and systems for approximately 10 years [...]
Highlights
This paper considers a system to generate a stereo 360 Virtual reality (VR) image using two cheap smartphones or two general purpose cameras attached to a rig
This paper proposes an efficient algorithm to conceal the holes in stereo 360 VR images, where camera parameters are utilized to estimate the location of the most similar fraction in other view images
The objective performances of the concealment algorithms were evaluated with common measures like the structural similarity (SSIM) [35], peak-to-peak signal-to-noise ratio (PSNR) [36], and consumed CPU time
Summary
Proposed a deep learning-based method to conceal a hole in a non-VR image, where convolution weights are normalized by the mask area of the window. This effectively prevents the convolution filters from capturing too many zeros when they traverse over the incomplete region. Note that certain conventional methods [16,17,18,19,20,21,22,23,24] use the data of the same camera stream to conceal the holes In these techniques, the holes are filled based on the neighboring pixels and their performances are limited when the size of the hole is large.
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