Title: Neural network-based arithmetic coding of intra prediction modes in HEVC,
Abstract: In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is
adopted as the entropy coding method. CABAC relies on manually designed
binarization processes as well as handcrafted context models, which may
restrict the compression efficiency. In this paper, we propose an arithmetic
coding strategy by training neural networks, and make preliminary studies on
coding of the intra prediction modes in HEVC. Instead of binarization, we
propose to directly estimate the probability distribution of the 35 intra
prediction modes with the adoption of a multi-level arithmetic codec. Instead
of handcrafted context models, we utilize convolutional neural network (CNN) to
perform the probability estimation. Simulation results show that our proposed
arithmetic coding leads to as high as 9.9% bits saving compared with CABAC.