Neural network-based arithmetic coding of intra prediction modes in HEVC
This work addresses compression efficiency for video coding in HEVC, representing an incremental improvement by replacing handcrafted components with neural networks.
The paper tackled the compression efficiency limitation of manually designed binarization and context models in HEVC's CABAC by proposing a neural network-based arithmetic coding strategy for intra prediction modes, resulting in up to 9.9% bits saving compared to CABAC.
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.