Region-adaptive probability model selection for the arithmetic coding of video texture

Abstract

In video coding systems using adaptive arithmetic coding to compress texture information, the employed symbol probability models need to be retrained every time the coding process moves into an area with different texture. To avoid this inefficiency, we propose to replace the probability models used in the original coder with multiple switchable sets of probability models. We determine the model set to use in each spatial region in an optimal manner, taking into account the additional signaling overhead. Experimental results show that this approach, when applied to H.264/AVC's context-based adaptive binary arithmetic coder (CABAC), yields significant bit-rate savings, which are comparable to or higher than those obtained using alternative improvements to CABAC previously proposed in the literature.

Higher precision range estimation for context‐based adaptive binary arithmetic coding

“…The main disadvantage of the HEVC approach is that it requires considerable multiplication in one step. A significant improvement proposed in previous work can be achieved by modification of the context modelling in CABAC [10, 11]. A better compression rate is obtained by using a look‐up‐table, index‐based entropy coder [7].…”

An Efficient Adaptive Binary Arithmetic Coder With Low Memory Requirement

“…Probability estimation for symbol is calculated as (13) and implemented by using tables and which contain number of the next probabilities after compression of the current symbol. It is important to notice that if we define , then it is easy to see that the probability estimation rule (13) is based on rule (11).…”

“…The main disadvantage of this approach is that a multiplication operation is required. Improving of compression efficiency can be also achieved by modification of the context modeling in CABAC [11]- [13] but it also accompanied by significant increase of computation complexity.…”