Lossless Compression of Mosaic Images with Convolutional Neural Network Prediction
This addresses a specialized problem for digital camera users and image processing applications, with incremental improvements in compression efficiency.
The paper tackles lossless compression of raw color mosaic images from digital cameras by introducing a CNN-based predictor, achieving unprecedented compression performance.
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.