In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras. Our approach utilizes sparse representation of epipolar-plane images (EPI) in shearlet transform domain. The shearlet transform has been specifically modified to handle the straight lines characteristic for EPI. The devised iterative regularization algorithm based on adaptive thresholding provides high-quality reconstruction results for relatively big disparities between neighboring views. The generated densely sampled light field of a given 3D scene is thus suitable for all applications which require light field reconstruction. The proposed algorithm compares favorably against state of the art depth image based rendering techniques and shows superior performance specifically in reconstructing scenes containing semi-transparent objects.

Light field (LF) acquisition devices capture spatial and angular information of the scene. In contrast with traditional cameras, the additional angular information enables novel post-processing applications such as 3D scene reconstruction, refocusing at different depth planes, and synthetic aperture. In this paper, we present a novel compression scheme for LF data captured using multiple traditional cameras. The input LF views are divided into two groups, i.e. key views and decimated views. The key views are compressed using multiview extension of High Efficiency Video Coding (MV-HEVC) scheme and decimated views are predicted using the shearlet transform based prediction (STBP) scheme. Additionally, the residual information of predicted views is also encoded and sent along with the coded stream of key views. The proposed scheme is evaluated over benchmark multi-camera based LF dataset and it is demonstrated that incorporating the residual information into compression scheme increases the overall PSNR by 2 dB. The proposed compression scheme performs significantly better in low bit-rates compared to anchor schemes whose compression efficiency is better in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts specifically in low bit-rates favors the proposed compression scheme over the anchor schemes.

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The experimentation was performed on two light field data sets: Stanford dataset and High Density Camera Array (HDCA) dataset. The rate-distortion analysis for the proposed compression scheme shows significant compression efficiency in low bit-rate scenarios as compared to the anchor compression scheme. However, the anchor performs better in high bit-rates. The sensitivity of human vision system towards the compression artifacts in low bit-rates favours the proposed compression scheme over the anchor. Figure 3: Rate Distortion analysis of proposed compression scheme with reference HEVC video compression standard on Stanford and HDCA LF images. ETN-FPI (Project number 676401) is funded under the H2020-MSCA-ITN-2015 call and is part of the Marie Sklodowska-Curie Actions-Innovative Training Networks (ITN) funding scheme

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