Context-based Adaptive Binary Arithmetic Coding (CABAC) is the entropy coding module in the HEVC/H video coding standard. As in its predecessor. High Throughput CABAC Entropy Coding in HEVC. Abstract: Context-adaptive binary arithmetic coding (CAB-AC) is a method of entropy coding first introduced . Context-based Adaptive Binary Arithmetic Coding (CABAC) is a method of entropy coding which is widely used in the next generation standard of video coding.

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Views Read Edit View history. CABAC has multiple probability modes for different contexts.

Context-Based Adaptive Binary Arithmetic Coding (CABAC) – Fraunhofer Heinrich Hertz Institute

On the lowest level of processing in CABAC, each bin value enters the binary arithmetic encoder, either in regular or bypass coding mode. By decomposing each syntax element value into a sequence of bins, further processing czbac each bin value in CABAC depends on the associated coding-mode decision, which can be either chosen as the regular or the bypass mode. Video Coding for Next-generation Multimedia.

As an important design decision, the latter case is generally applied to the most frequently observed bins only, whereas the other, usually less frequently hebc bins, will be treated using a joint, typically zero-order probability model. Utilizing suitable context models, a given inter-symbol redundancy can be exploited by switching between different probability models according to already-coded symbols in the neighborhood of the current symbol to encode.

The L1 norm of two previously-coded values, e kis calculated:. Related standard contributions in chronological order, as listed here: Pre-Coding of Transform-Coefficient Levels Coding of residual data in CABAC involves specifically designed syntax elements that are different from those used in the traditional run-length pre-coding approach. This allows the discrimination of statistically different sources with the result of a nevc better adaptation to the individual statistical characteristics.

However, in comparison to this research work, additional aspects previously largely ignored have been taken into account during the development of CABAC. These aspects are mostly related to implementation complexity and additional requirements in terms of conformity and applicability. From that time until completion of the first standard specification of H. Support of additional coding tools such as interlaced coding, variable-block size transforms as considered for Version 1 of H.


Coding-Mode Decision and Context Modeling By decomposing each syntax element value into a sequence of bins, further processing of each bin value in CABAC depends on the associated coding-mode decision, which can be either chosen as the regular or the bypass mode. Usually the addition of syntax elements also affects the distribution of already available syntax elements which, in general, for a VLC-based entropy-coding approach may require to re-optimize the VLC tables of the given syntax elements rather than just adding a suitable VLC code for the new syntax element s.

The selected context model supplies hev probability estimates: In the following, we will present some important aspects of probability estimation in CABAC that are not intimately tied to the M coder design.

On the lower level, there is the quantization-parameter dependent initialization, which is invoked at the beginning of each slice. The context modeling provides estimates of jevc probabilities of the coding symbols. Context-modeling for coding of binarized level magnitudes are based on hevv number of previously transmitted level magnitudes greater or equal to 1 within the reverse scanning path, which is motivated by the observation that levels with magnitude equal to 1 are statistical dominant at the end of the scanning path.

The design of binarization schemes in CABAC is based on a few elementary prototypes whose structure enables simple online calculation and which are adapted to some suitable model-probability distributions. For each block with at least one nonzero quantized transform coefficient, a sequence of binary significance flags, indicating the position of significant i. The specific features and the underlying design principles of the M coder can be found here.

Since the encoder can choose between the corresponding three tables of initialization parameters and signal its choice to the decoder, an additional degree of pre-adaptation is achieved, especially in the case of using small slices at low to medium bit rates.

The design of these four prototypes is based on a priori knowledge about the typical characteristics of the source data to be modeled and it reflects the aim to find a good compromise between the conflicting objectives of avoiding unnecessary modeling-cost overhead and exploiting the statistical dependencies to a large extent.

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It has three distinct properties:. Each probability model in CABAC can take one out of different states with associated probability values p ranging in the interval [0. CABAC is also difficult to parallelize and vectorize, so other forms of parallelism such as spatial region parallelism may be coupled with its use.

It first converts all non- binary symbols to binary. Probability estimation in CABAC is based on a table-driven estimator using a finite-state machine FSM approach with tabulated transition rules as illustrated above. Arithmetic coding is finally applied to compress the data.

The arithmetic decoder is described in some detail in the Standard. CABAC is notable for providing much better compression than most other entropy encoding algorithms used in video encoding, and it is one of the key elements that provides the H. One of 3 models is selected for bin 1, based on previous coded MVD values. For the specific choice of context models, four basic design types are employed in CABAC, where two of them, as further described below, are applied to coding of transform-coefficient levels, only.

Context-Based Adaptive Binary Arithmetic Coding (CABAC)

However, in cases where the amount of data in the process of adapting to the true underlying statistics is comparably small, it is useful to provide some more appropriate initialization values for each probability model in order to better reflect its typically skewed nature. Javascript is disabled in your browser.

In the regular coding mode, each bin value is encoded by using the regular binary arithmetic-coding engine, where the associated probability model is either determined by a fixed choice, without any context modeling, or adaptively chosen fabac on the related context model. By using this site, you agree to the Terms of Use and Privacy Policy.