Chroma Block Prediction with Reduced Multiplication Complexity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing method for chroma block prediction in video encoding involves complex calculations due to the determination of scaling coefficients and offset factors for each chroma block, leading to low prediction efficiency.
Innovation Solution
A method that involves obtaining chroma and luma values from neighboring samples, classifying them into sets, and determining a scaling coefficient and offset factor based on average values to reduce the number of multiplications required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the cross-component linear mode (CCLM) is used to determine prediction information for each chroma block, then prediction accuracy is improved, but calculation complexity increases due to the large quantity of multiplications required
Solution Approach 1:
The chroma block is divided into multiple sub-blocks, and the scaling coefficient and offset factor are determined separately for each sub-block rather than for the entire chroma block. This segmentation reduces the calculation complexity by allowing parallel processing and reducing the number of multiplications required for each individual sub-block prediction.
Solution Approach 2:
The patent applies the CCLM mode selectively to only certain chroma blocks or sub-blocks where it provides significant prediction improvement, rather than uniformly applying it to all chroma blocks. This partial application reduces the overall calculation complexity while maintaining prediction accuracy in regions where it is most beneficial.
2Measurement precision
If the cross-component linear mode (CCLM) is used to determine prediction information for each chroma block, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
By segmenting the chroma block into sub-blocks and determining prediction parameters for each sub-block independently, the patent enables parallel processing of multiple sub-blocks simultaneously, thereby reducing the total processing time while maintaining prediction accuracy.
Solution Approach 2:
The patent performs preliminary calculations of the scaling coefficient and offset factor based on neighboring luma and chroma samples before the actual prediction process. This preliminary action prepares the necessary parameters in advance, reducing the processing time during the main prediction and encoding stages.
3Manufacturing precision
If a scaling coefficient and offset factor are determined for each chroma block using the CCLM mode, then prediction quality is improved, but the number of multiplications increases
Solution Approach 1:
The chroma block is divided into multiple sub-blocks, and the scaling coefficient and offset factor are determined separately for each sub-block. This segmentation reduces the total number of multiplications required compared to determining single parameters for the entire chroma block, as each sub-block requires fewer calculations.
Solution Approach 2:
The patent modifies the CCLM mode by changing how parameters are determined - instead of calculating scaling coefficients and offset factors for each individual chroma block, it calculates them for sub-blocks or applies simplified parameter determination methods, thereby reducing the number of multiplications while maintaining prediction quality.
Data Source
AI summary
This application provides a chroma block prediction method and apparatus. The method includes: obtaining chroma values of chroma samples; obtaining, luma values of luma samples corresponding to the chroma samples; obtaining, from the luma values, a first luma set and a second luma set; grouping the chroma values into a first chroma set and a second chroma set; determining a scaling coefficient in a linear model based on an average value of luma values in the first luma set, an average value of luma values in the second luma set, an average value of chroma values in the first chroma set, and an average value of chroma values in the second chroma set; determining, based on the scaling coefficient, an offset factor in the linear model; and determining prediction information of the chroma block based on the scaling coefficient and the offset factor.