3D holoscopic image coding scheme using HEVC with Gaussian process regression

3D holoscopic image, also known as integral imaging, light field imaging and plenoptic imaging, can provide a natural and fatigue-free 3D visualization. However, a large amount of data is required to represent the 3D holoscopic content. Therefore, efficient coding schemes for such particular type of image are needed. In this paper, we propose a Gaussian process regression based prediction scheme to compress the 3D holoscopic image. In the proposed scheme, the coding block and its prediction supports are modeled as a Gaussian process (GP) and Gaussian process regression (GPR) is used to obtain a better prediction of the coding block. Limited searching windows in horizontal and vertical directions are used to obtain the prediction supports, and a filtration method is designed to judge the reliability of the obtained prediction supports. Moreover, in order to alleviate the high complexity caused by GPR, a sparsification method is also put forward. Experimental results demonstrate the advantage of the proposed scheme for 3D holoscopic image coding in terms of different quality metrics as well as the visual quality of the views rendered from decompressed 3D holoscopic content, compared to the HEVC intra-prediction method and several other prediction methods in this field.