Bitwise Structured Prediction Model for Lossless Image Coding

Wenrui Dai, Hongkai Xiong

Abstract: In this paper, we propose the bitwise structured prediction model for lossless image coding, especially for the oscillatory regions. The learning-based model utilizes the regular features obtained from the predicted local data. At first, the pixel-wise prediction is decomposed into the bitwise ones. In each bit plane, the prediction of the current bit is simplified to the max margin estimation for the 0/1 prediction problem and obtained directly conditioned on the neighboring predicted bits. Furthermore, since the decreasing dependencies of neighboring bits in lower bit plane lead to the turbulence of predictive results, the structured prediction is proposed to establish the Markov network to constrain the outputs of the bit planes, and suppress the prediction errors with a well-defined loss function. Consequently, the min-max formulation is proposed for the concurrent optimization for maximizing the 0/1 margin of all the bit planes.