Example
1. Binarize the value MVDx .
The first bit of the binarized codeword is bin 1; the second bit is bin 2; and so on.
2. Choose a context model for each bin. One of 3 models is selected for bin 1, based on previous coded MVD values. The L1 norm of two previously-coded values, ek, is calculated:
If ek is small, then there is a high probability that the current MVD will have a small magnitude; conversely, if ek is large then it is more likely that the current MVD will have a large magnitude. We select a probability table (context model) accordingly. The remaining bins are coded using one of 4 further context models:
3. Encode each bin. The selected context model supplies two probability estimates: the probability that the bin contains “1” and the probability that the bin contains “0”. These estimates determine the two sub-ranges that the arithmetic coder uses to encode the bin.
4. Update the context models. For example, if context model 2 was selected for bin 1 and the value of bin 1 was “0”, the frequency count of “0”s is incremented. This means that the next time this model is selected, the probability of an “0” will be slightly higher. When the total number of occurrences of a model exceeds a threshold value, the frequency counts for “0” and “1” will be scaled down, which in effect gives higher priority to recent observations.
Read more about this topic: Context-adaptive Binary Arithmetic Coding
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