CABAC Renormalization Prediction for Continuous Symbol Encoding
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Solution Overview
Problem
Existing video data compression technologies, such as H.264/AVC, face performance degradation due to the computationally intensive and time-consuming process of renormalization in context-based adaptive binary arithmetic coding (CABAC), which waits for renormalization to complete before encoding subsequent symbols.
Innovation Solution
The system predicts the result of renormalization for a symbol being encoded and uses this prediction to encode the next symbol, eliminating the need to wait for renormalization completion, with a processing module completing renormalization post-processing to generate an encoded bitstream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If renormalization is performed completely before encoding the next symbol, then encoding accuracy is maintained, but encoding speed deteriorates due to waiting for renormalization completion
Solution Approach 1:
The patent performs preliminary renormalization by predicting the number of renormalizations needed and pre-calculating the renormalized interval range before encoding the current symbol. This allows the encoder to proceed without waiting for complete renormalization, thus improving encoding speed while maintaining sufficient accuracy through the prediction mechanism.
Solution Approach 2:
The patent introduces dynamic prediction of renormalization count based on the current interval range state. The prediction module dynamically adjusts the number of renormalizations to be performed based on real-time encoder state, allowing the system to adapt between accuracy and speed requirements depending on the encoding context.
2Productivity
If renormalization prediction is used to encode next symbol earlier, then encoding efficiency improves, but computational complexity increases due to prediction requirements
Solution Approach 1:
The prediction module performs preliminary estimation of renormalization count using simple operations on the current interval range. This preliminary action provides enough information to proceed with encoding without requiring complex real-time renormalization calculations, thus improving efficiency while keeping the added complexity manageable.
Solution Approach 2:
The prediction module acts as an intermediary between the interval range management and symbol encoding processes. It provides predicted renormalization information that allows the encoder to proceed without direct dependency on complete renormalization, simplifying the control flow while enabling parallel processing.
Data Source
AI summary
A system including a binarization module, a prediction module, and a shifting module. The encoding module is configured to encode symbols using context-adaptive binary arithmetic coding, in which the symbols are generated by binarizing a syntax element. The prediction module is configured to generate a prediction for a number of renormalizations to be performed to renormalize an interval range when encoding one of the symbols. The shifting module is configured to generate a renormalized interval range by shifting the binarized syntax element R times, where R is a number of leading zeros before a 1 in the binarized syntax element. The encoding module is configured to encode a next symbol following the one of the symbols based on the renormalized interval range.