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 first converts all non- binary symbols to binary. 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.
For each block with at least one nonzero quantized transform coefficient, a sequence of binary significance flags, indicating the position of significant i. At that time – and also at a later stage when the scalable extension of H. 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.
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. 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.
These elements are illustrated as the main algorithmic building blocks of the CABAC encoding block diagram, as shown above. 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.
Context-adaptive binary arithmetic coding – Wikipedia
Interleaved with these significance flags, a sequence of so-called last flags one for each significant coefficient level cabqc generated for signaling the position of the last significant level within the scanning path. 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.
Retrieved from ” https: 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 hhevc. The specific features and the underlying design principles of the M coder can be found here. Related standard contributions in chronological order, as listed here: 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.
On the lower level, there is the quantization-parameter dependent initialization, which is invoked at the beginning of each slice. 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. One of 3 models is selected for bin 1, based on previous coded MVD values.
Cabxc however that the actual transition rules, as tabulated in CABAC and as shown in the graph above, were determined to be only approximately equal to those derived by this exponential aging rule. 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.
In the following, we will present some important aspects of probability estimation in CABAC that are not intimately tied to the M coder design.
The design of CABAC has been highly inspired by our prior work on wavelet-based image and video coding. 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 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. CABAC is also difficult to parallelize and vectorize, so other forms of parallelism such as spatial region parallelism may be coupled with its use. Views Read Edit View history. From Wikipedia, the free encyclopedia. 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.
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. The L1 norm of two previously-coded values, e kis calculated:. The design of CABAC involves the key elements of binarization, context modeling, and binary arithmetic coding.
Support of additional coding tools such as interlaced coding, variable-block size transforms as considered for Version 1 of H. 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.
Update the context models. 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.
On the lowest level of processing in CABAC, each bin value enters the binary arithmetic encoder, either in regular or bypass coding mode.
Context-Based Adaptive Binary Arithmetic Coding (CABAC)
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.
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 the CABAC design.
It is a lossless compression technique, although the video coding standards in which it is used are typically for lossy compression applications. The context modeling provides estimates of conditional probabilities of the coding symbols.
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. We select a probability table context model accordingly. 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.
This is the purpose of the initialization process for context models in CABAC, which operates on two levels. The selected context model supplies two probability estimates: Czbac and Systems for Video TechnologyHvec. Each probability model in CABAC can take one out of different states with associated probability values p ranging in cabaf interval [0.
cabaac This allows the discrimination of statistically different sources with the result of a significantly better adaptation to the individual statistical characteristics. The arithmetic decoder is described in some detail in the Standard. 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.
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.