Higher precision range estimation for context‐based adaptive binary arithmetic coding
Sio‐Kei Im, Ka‐Hou Chan · IET Image Processing · 2019
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.