Lossless Compression of Plenoptic Camera Sensor Images

Abstract

We propose a codec for the lossless compression of plenoptic camera sensor images. The proposed encoder starts by splitting the input lenslet image into rectangular patches, with each patch corresponding to a microlens image. The encoder and decoder exploit the correlation between the pixels in neighbor patches using a patch-by-patch prediction mechanism where each pixel of a patch has its own dedicated sparse predictor designed to utilize the most relevant pixels from the neighbor patch to the left. An intra-patch prediction mask together with the pixels from the neighbor left patch form the final prediction template. The encoder performs the design of sparse predictors by first finding the relevant regressors in the final template. The patches are classified into M classes according to two possible mechanisms (either based on depth information or based on Bayer mask colors), and the sparse predictor design is performed for each pair (class label; patch pixel index). A relevant context selection mirrors the selection of relevant regressors, thereby providing the arithmetic coding with skewed coding distributions at each context. We show examples of the application of the proposed methods on sensor images from the JPEG Pleno database, thus demonstrating the improved performance of the proposed methods compared to the existing predictive methods for encoding camera sensor images.