Coding of plenoptic images by using a sparse set and disparities

A focused plenoptic camera not only captures the spatial information of a scene but also the angular information. The capturing results in a plenoptic image consisting of multiple microlens images and with a large resolution. In addition, the microlens images are similar to their neighbors. Therefore, an efficient compression method that utilizes this pattern of similarity can reduce coding bit rate and further facilitate the usage of the images. In this paper, we propose an approach for coding of focused plenoptic images by using a representation, which consists of a sparse plenoptic image set and disparities. Based on this representation, a reconstruction method by using interpolation and inpainting is devised to reconstruct the original plenoptic image. As a consequence, instead of coding the original image directly, we encode the sparse image set plus the disparity maps and use the reconstructed image as a prediction reference to encode the original image. The results show that the proposed scheme performs better than HEVC intra with more than 5 dB PSNR or over 60 percent bit rate reduction.