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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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.

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. Video Coding for Next-generation Multimedia. Binarization 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.

The other method specified in H. For each block with at least one nonzero quantized transform coefficient, a sequence of binary significance flags, indicating the position of significant i. Probability Estimation 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. 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.

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. Retrieved from ” https: Views Read Edit View history. 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.

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. 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.


It has three distinct properties:. On the lowest level of processing in CABAC, each bin value enters the binary arithmetic encoder, either in regular or bypass coding mode.

Context-adaptive binary arithmetic coding – Wikipedia

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 binarization 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 hvec CABAC design.

We select a probability table context model accordingly. However, in comparison to this research work, additional aspects previously largely ignored have been taken into account during the development of CABAC.

Update the context models. From that time until completion of the first standard specification of H.

Context-Based Adaptive Binary Arithmetic Coding (CABAC)

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 significant bins, which are assumed to be uniformly distributed and for which, consequently, the whole regular binary arithmetic encoding process is simply bypassed. 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 depends 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.

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. 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. 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 is a lossless compression technique, although the video coding standards in which it is used are typically for lossy compression applications. 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. Choose a context model for each bin. 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.


For acbac specific choice of context models, cabzc basic design types are employed in CABAC, where two of them, as further described below, are applied to coding of transform-coefficient levels, only. Then, for each bit, the coder selects which probability model to use, then uses information from nearby elements to optimize the probability estimate. The remaining bins are coded using one of 4 further context models:.

Circuits and Systems for Video TechnologyVol. These aspects are mostly related to implementation complexity and additional requirements in terms of conformity and applicability.

Please enable it for full functionality and experience. The L1 norm of two previously-coded values, e kis calculated:.

Context-adaptive binary arithmetic coding

The selected context model supplies two probability estimates: The context modeling provides estimates of conditional probabilities of the coding symbols. 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.

One of 3 models is selected for bin 1, based on cabad coded MVD values. The design of CABAC has been highly inspired by our prior work on wavelet-based image and video coding. 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. From Wikipedia, the free encyclopedia.

The definition of the decoding process is designed to facilitate low-complexity implementations of arithmetic heevc and 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.

Support of additional coding tools such as interlaced coding, variable-block size transforms as considered for Version 1 of H. Arithmetic coding is cqbac applied to compress the data. 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.