Lossless Compression of Raw Images by Learning the Prediction and Frequency Decomposition

This paper presents a lossless color filter array (CFA) image compression method that attempts to maximize the use of image correlations from two perspectives. First, we adopt a hierarchical approach for decomposing an input into subimages and proceed with the subimage encoding with a new encoding order. The new encoding scheme is shown to provide better prediction performances compared to conventional raster scan orders. Secondly, for each subimage, we compress the low-frequency components first and use them as additional input for encoding the remaining high-frequency components. In this scenario, the high-frequency components show improved compression efficiency since they use the low-frequency ones as strong prior. Experiments show that the proposed method achieves state-of-the-art performance for 8∼16 bits raw images.