  We present a CNN-based predictive lossless compression scheme for raw color
mosaic images of digital cameras. This specialized application problem was
previously understudied but it is now becoming increasingly important, because
modern CNN methods for image restoration tasks (e.g., superresolution, low
lighting enhancement, deblurring), must operate on original raw mosaic images
to obtain the best possible results. The key innovation of this paper is a
high-order nonlinear CNN predictor of spatial-spectral mosaic patterns. The
deep learning prediction can model highly complex sample dependencies in
spatial-spectral mosaic images more accurately and hence remove statistical
redundancies more thoroughly than existing image predictors. Experiments show
that the proposed CNN predictor achieves unprecedented lossless compression
performance on camera raw images.
