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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This allows the discrimination of statistically different sources with the result of a significantly better adaptation to the individual statistical characteristics. By using this site, you agree to the Terms of Use and Privacy Policy.

If e k is small, then there is a high probability that the current MVD will have a small magnitude; czbac, if e k is large then it is more likely that the current MVD will have a large magnitude. At that time – and also at a later stage when the scalable extension of H. These aspects are mostly related to implementation complexity and additional requirements in terms of conformity and applicability. Update the context models. The definition of the decoding process is designed to facilitate low-complexity implementations of arithmetic encoding and decoding.

Context-adaptive binary arithmetic coding

The specific features and the underlying design principles of the M coder can be found 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.

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.

Context-modeling for coding of binarized level magnitudes are based on the 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. However, in comparison to this research work, additional aspects previously largely ignored have been taken into account during the development of CABAC.

This so-called significance information is transmitted as a preamble of the regarded transform block followed by the magnitude and sign information of nonzero levels in reverse scanning order. The latter is chosen for bins related to the sign information or for lower significant bins, which are assumed to be uniformly distributed and for which, consequently, the whole regular binary arithmetic encoding process is simply bypassed.

As a consequence of these important criteria within any standardization effort, additional constraints have been imposed on the design of CABAC with the result that some of its original algorithmic components, like the binary arithmetic coding engine have been completely re-designed.


These estimates determine the two sub-ranges that the arithmetic coder uses to encode the bin. Support of additional coding tools such as interlaced coding, variable-block size transforms as considered for Version 1 of Caac.

These elements are illustrated as the main algorithmic building blocks of the CABAC encoding block diagram, cabaac shown above. 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 depending on the related context model.

In the following, we will present some important aspects of probability estimation in CABAC that are not intimately tied to the M coder design.

It has three distinct properties:. Redesign of VLC tables is, however, a far-reaching structural change, which may not be justified for the addition of a single coding tool, especially if it relates to an optional feature only. 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.

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 caabac small slices at low to medium bit rates. CABAC is based on arithmetic codingwith a few innovations and changes to adapt it to the needs cabsc video encoding standards: The coding strategy of CABAC is based on the finding that a very efficient coding of syntax-element values in a hybrid block-based video coder, like components hefc motion vector differences or transform-coefficient level values, can be achieved by employing a binarization scheme as a kind of preprocessing unit for the subsequent stages of context modeling and binary arithmetic coding.

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 hevf.

This is the purpose of the initialization process for context models in CABAC, which operates on two levels. It turned out that in contrast to entropy-coding schemes based on variable-length codes VLCsthe CABAC coding approach offers an additional advantage in terms of extensibility such that the support of newly added syntax elements can be achieved in a more simple and fair manner.

The remaining bins are coded using one of 4 further context models:. From Wikipedia, the free encyclopedia. Arithmetic coding is finally applied to compress the data. Usually the addition of syntax elements also affects the distribution of already available syntax elements which, cabaf 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.


It generates an initial state value depending on the given slice-dependent quantization parameter SliceQP using a pair of so-called initialization parameters for each model which describes a modeled linear relationship between the SliceQP and the model probability p.

Application-Specific Cache and Prefetching for HEVC CABAC Decoding

In this way, CABAC enables selective context modeling on a sub-symbol level, and hence, provides an efficient instrument for exploiting inter-symbol redundancies at significantly reduced overall modeling or learning costs. Each probability model in CABAC can take one out of different states with associated probability values p ranging in the interval [0.

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. Then, for each bit, the coder selects which probability model to use, then uses information from nearby elements to cabxc the probability heevc. 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.

Note however that the actual transition rules, hev tabulated in CABAC and as shown in the graph above, were determined to be only approximately equal to those csbac by this exponential aging rule. On the lowest level of processing in CABAC, each bin value enters the binary arithmetic encoder, either in regular or bypass coding hevx. Retrieved from ” https: Since CABAC guarantees an inherent adaptivity to the actually given conditional probability, there is no need for further structural adjustments besides the choice of a dabac or context model and associated initialization values which, as a first approximation, can be chosen in a canonical way by using the prototypes already specified in the CABAC design.

The selected context model supplies two probability estimates: The design of CABAC involves the key elements of binarization, context modeling, and binary arithmetic coding. As an important design decision, the latter case is generally applied to the most frequently observed bins only, whereas the other, usually less frequently observed bins, will be treated using a joint, typically zero-order probability model.

From that time until completion of the first standard specification of H. Hevcc standard contributions in chronological order, as listed here: CABAC is also difficult to parallelize and vectorize, so other forms of parallelism such as spatial region parallelism may be coupled with its use.

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.