Comparing Pixel Predictors for Lossless Image Coding

The efficiency of lossless image coding depends on the pixel predictors, with which unknown pixels are predicted from already-processed pixels. Recent advances in deep learning brought new tools that can be used for pixel prediction, such as deep convolutional neural networks (CNNs). In this paper, we focus on the processing order of the pixels and propose a new pixel predictor constructed using CNNs. Instead of the conventional scanline order, we design a new processing order where the pixels are processed in a progressive, parallelizable manner and the reference pixels are located in all directions with respect to a target pixel. Our pixel predictor is implemented using a CNN architecture that was originally developed for image inpainting, a task of filling in missing pixels from known pixels in an image. We compare the performance of our method against the conventional scanline-based CNN in terms of the potential coding efficiency and computational cost.