# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 1 Introduction
[16] proposed an inpainting algorithm based on fluid dynamics, where a two-dimensional Navier-Stokes equation is approximated and combined with the Poisson equation for the vorticity. This approach produces an approximate solution for the holes in non-VR images. In the algorithm proposed by [PERSON] et al. [17], each pixel in a hole is progressively concealed from the boundary to the center of the hole, where the value of a pixel to be concealed is set by the weighted average of the neighbouring pixels. [PERSON] et al. [18] proposed a method to conceal a hole by filling it using a block. In this algorithm, the most similar patches are searched over a region around the hole. This scheme is a kind of template matching technique and the squared sum of the difference (SSD) is used as a criterion. On other hand, algorithms based on deep learning techniques were proposed in [19; 20; 21; 22; 23; 24]. [PERSON] et al. [19] 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. When the deep learning-based algorithm is applied, the coefficients and variables in the convolutional network should be optimized for the huge quantity of training data. Thus, it requires a long training time and the performance depends on the type of training data. 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 therefore their performances are limited when the size of the hole is large. Unlike those techniques [16; 17; 18; 19; 20; 21; 22; 23; 24], when we fill the holes in the stereoscopic VR images, we use the data from the opposite camera stream. Using the data of the opposite camera stream is more efficient than using the data from the same camera stream, because the left and right views are correlated with each other. In order to compare the performances of two categories (using the data from same camera stream and opposite camera streams), we provide the simulation results obtained by methods in two categories in Section 4. 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. This paper is organized as follows. In Section 2, we formulate the problem, where two general purpose cameras are used to capture pictures for the left and right views. These pictures are used to generate stereo 360 VR images, where some blocks related to a hole are defined. In Section 3, we propose an efficient algorithm to conceal the holes, where the geometrical property of the pixels in VR images and the relationship between them are analyzed. Simulation results that demonstrate the performance of the proposed algorithm are provided in Section 4, where the performances of various techniques are compared subjectively and objectively. In Section 5, we provide a brief conclusion for this paper.