Neural network-based arithmetic coding of intra prediction modes in HEVC
arXiv:1709.05737 · doi:10.1109/VCIP.2017.8305104
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.
arXiv commentVCIP 2017
Cited by in corpus (7)
- Image and Video Compression with Neural Networks: A Review
- BVI-DVC: A Training Database for Deep Video Compression
- Deep Learning-Based Video Coding: A Review and A Case Study
- Learning for Video Compression
- MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering
- Estimation of Rate Control Parameters for Video Coding Using CNN
- Immersive Video Compression using Implicit Neural Representations