Higher precision range estimation for context-based adaptive binary arithmetic coding
The Lagrangian rate distortion optimisation is widely employed in modern video encoders, such as high-efficiency video coding (H.265/HEVC). In this work, the authors propose a more accurate context-based adaptive binary arithmetic coding look-up table that can enhance compression quality and provide substantially better accuracy of range estimation, by employing one-more bit with 64 probability states. For the hardware implementation, they propose a higher precision look-up table instead of the HEVC Test Model (HM) standard table. The authors also define a new finite-state machine to handle the probability changing in real-time. The significant BD-RATE gain of the proposed context modelling is up to 6.0% for all-intra mode and 13.0% for inter mode. This finite state machine offers no divergence from the H.265/HEVC standards and can be used in the current systems.
