CNN-based Prediction for Lossless Coding of Photographic Images

The paper proposes a novel prediction paradigm in image coding based on Convolutional Neural Networks (CNN). A deep neural network is designed to provide accurate pixel-wise prediction based on a causal neighbourhood. The proposed CNN prediction method is trained on the high-activity areas in the image and it is incorporated in a lossless compression system for high-resolution photographic images. The system uses the proposed CNN-based prediction paradigm as well as LOCO-I, whereby the predictor selection is performed using a local entropy-based descriptor. The prediction errors are encoded using a CALIC-based reference codec. The experimental results show a good performance for the proposed prediction scheme compared to state-of-the-art predictors. To our knowledge, the paper is the first to introduce CNN-based prediction in image coding, and demonstrates the potential offered by machine learning methods in coding applications.