CCLM Prediction Parameter Derivation Using Logarithmic Scaling
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Solution Overview
Problem
In CCLM processing, large memory usage is a problem due to the need for tables in deriving linear prediction parameters for generating prediction images.
Innovation Solution
A moving image decoding device and method that derive a first and second parameter using a sampled luminance value downsampled according to a chrominance format and an intra-frame prediction mode, where the first parameter is derived by calculating a logarithmic value of a luminance difference value and using a reference table to determine a fourth value, and the second parameter is derived using the first parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tables are used to derive linear prediction parameters in CCLM processing, then prediction accuracy is improved, but memory usage increases
Solution Approach 1:
The patent changes the parameter representation by using logarithmic values of luminance difference values as indices for the reference table. This transformation allows the system to maintain prediction accuracy while reducing the table size, as logarithmic scaling compresses the range of values needed for accurate prediction.
Solution Approach 2:
The patent applies partial action by using only the necessary portion of the luminance difference range for table lookup. By right-shifting the second value by the logarithmic value, the system accesses only the critical parts of the data range, reducing memory requirements while maintaining sufficient prediction accuracy.
2Speed
If integer operations and table lookups are used to derive linear prediction parameters, then processing speed is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex multiplication operations with simpler right-shift operations. By using the logarithmic value to determine the shift amount, the system achieves fast integer division without requiring complex hardware dividers, thus maintaining processing speed while reducing device complexity.
Solution Approach 2:
The patent performs preliminary computation by pre-calculating logarithmic values and storing them in a compact reference table. This allows the main processing to use simple table lookups and bit shifts rather than complex real-time calculations, maintaining speed while reducing operational complexity.
Data Source
AI summary
A moving image decoding method is provided for generating a prediction image using an intra-frame prediction mode. The moving image decoding method includes deriving a first parameter and a second parameter by using a sampled luminance value down-sampled according to a chrominance format and the intra-frame prediction mode; and deriving the prediction image by using the first parameter and the second parameter, wherein: the first parameter is derived by: deriving a logarithmic value of a luminance difference value, deriving a first value by right-shifting a second value related to the luminance difference value by the logarithmic value, and using a third value acquired by multiplying a fourth value by a chrominance difference value, wherein the fourth value is determined from a reference table by using the first value, and the second parameter is derived by using the first parameter.