MACRO-PIXEL PREDICTION BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR LOSSLESS COMPRESSION OF LIGHT FIELD IMAGES

The paper introduces a novel macro-pixel prediction method based on Convolutional Neural Networks (CNN) for lossless compression of light field images. In the proposed method, each macro-pixel is predicted based on a volume of macro-pixels from its immediate causal neighborhood. The proposed deep neural network operates on these macro-pixel volumes and provides accurate macro-pixel prediction in light field images. The resulting macro-pixel residuals are encoded by a reference codec built based on the CALIC codec. A context modeling method for light field images is proposed. Experimental results on a large light field image dataset show that the proposed prediction method systematically and substantially outperforms state-of-the-art predictors. To our knowledge, the paper is the first to introduce deep-learning based prediction of macro-pixels, enabling efficient lossless compression of light field images.