HEVC CABAC PDF

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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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.

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. As an extension of this low-level pre-adaptation of probability models, CABAC provides two additional pairs of initialization parameters for each model that is used in predictive P or bi-predictive B slices.

In general, a binarization scheme defines a unique mapping of syntax element values to sequences of binary decisions, so-called bins, which can also be interpreted in terms of a binary code tree.

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 remaining bins are coded using one of 4 further context models:.

Context-adaptive binary arithmetic coding – Wikipedia

The design of CABAC involves the key elements of binarization, context modeling, and binary arithmetic coding. 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.

Support of additional coding tools such as interlaced coding, variable-block size transforms as considered for Version 1 of H. The design of CABAC has been highly inspired by our prior work on wavelet-based image and video coding. If e k is small, then there is a high probability that the current MVD will have a small magnitude; conversely, if e k is large then it is more likely that the current MVD will have a large magnitude.

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

The selected context model supplies two probability estimates: 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.

It turned hev 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 other method specified in H.

These elements are illustrated as the main algorithmic building blocks of the CABAC encoding block diagram, as shown above. 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-adaptive binary arithmetic coding

The design of binarization schemes in CABAC is based on a few elementary prototypes whose structure cagac simple online calculation and which are adapted to some suitable model-probability distributions.

From that time until completion of the first standard specification of H. For the latter, a fast branch of the coding engine with a considerably reduced complexity is used while for the former coding mode, encoding of the given bin value cabc on the actual state of the associated adaptive probability model that is passed along with the bin value to the M coder – a term that has been chosen for the novel table-based binary arithmetic coding engine in CABAC. 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.

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 hrvc more appropriate initialization values for each probability model in order to better reflect its typically skewed cbaac. This allows the discrimination of statistically different sources with the result of a significantly better adaptation to the individual statistical characteristics.

This is the purpose of the initialization process for context models in CABAC, which operates on two levels.

On the lower level, there is the quantization-parameter dependent initialization, which is invoked at the beginning of each slice.

These estimates determine the two sub-ranges that the arithmetic coder uses to encode the bin. The latter is chosen for bins related to the sign information or for lower cabbac bins, which are assumed to be uniformly distributed and for which, consequently, the whole regular binary arithmetic encoding process is simply bypassed. Update the context models. Choose a context model for each bin. CABAC is based on arithmetic codingwith a few innovations and changes to adapt it to the yevc of 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 of 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.

On the lowest level of processing in CABAC, each bin value enters the binary arithmetic encoder, either in regular or bypass coding mode.