Chroma Block Prediction Using Luma Difference Segmentation
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Solution Overview
Problem
Existing technologies face challenges in determining intra prediction model parameters for chroma blocks efficiently, leading to low prediction efficiency due to complex calculations.
Innovation Solution
A method and apparatus for chroma block prediction that involves obtaining maximum and minimum luma values from neighboring samples, processing differences to reduce bit depth, and determining intra prediction model parameters based on chroma values and processed differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the cross-component linear mode (CCLM) is used to determine intra prediction model parameters for each chroma block, then prediction accuracy is improved, but calculation complexity increases and prediction efficiency decreases
Solution Approach 1:
The patent divides the chroma block processing into different cases based on the difference between maximum and minimum luma values. When the difference is small (≤ threshold), a simplified method is used; when the difference is large (> threshold), the full CCLM method is applied. This segmentation allows the system to achieve good prediction accuracy for most blocks while reducing calculation complexity for cases where full precision is not needed.
Solution Approach 2:
The patent introduces a threshold parameter to control the switching between simplified and full CCLM methods. By changing this parameter, the system can adjust the balance between prediction accuracy and calculation complexity. Additionally, the patent uses bit depth processing to reduce the precision of intermediate calculations while maintaining final prediction accuracy.
2Measurement precision
If the cross-component linear mode (CCLM) is used to determine intra prediction model parameters for each chroma block, then prediction accuracy is improved, but prediction efficiency decreases
Solution Approach 1:
The patent segments chroma block processing into fast-path and slow-path cases based on luma value variation. The fast path uses a simplified prediction method for blocks with small luma differences, while the slow path applies full CCLM for blocks requiring higher precision. This segmentation significantly improves overall prediction efficiency by avoiding complex calculations for blocks that don't need them.
Solution Approach 2:
The patent applies partial CCLM processing by using bit depth reduction for intermediate calculations. Instead of using full precision throughout the entire calculation process, the patent uses reduced precision (e.g., 5-bit or 6-bit) for intermediate steps and only applies full precision where necessary for final results. This partial application of full precision maintains accuracy while improving efficiency.
3Measurement precision
If full precision is used in calculating intra prediction model parameters, then prediction accuracy is improved, but calculation complexity and processing time increase
Solution Approach 1:
The patent changes the precision parameter dynamically based on the input characteristics. When the difference between maximum and minimum luma values is small, the patent uses reduced precision (bit depth reduction) for calculations. When the difference is large, full precision is restored. This dynamic parameter adjustment reduces processing time for most cases while maintaining accuracy when needed.
Solution Approach 2:
The patent uses simplified approximation methods for intermediate calculations that can be discarded after use. By using reduced-precision arithmetic for intermediate steps and only applying full precision for final results, the patent creates a hierarchy of calculation quality where cheaper, faster operations are used where sufficient, and more expensive operations are reserved for critical final computations.
Data Source
AI summary
This application provides a chroma block prediction method and apparatus. The method includes: obtaining a maximum luma value and a minimum luma value based on luma samples corresponding to neighboring samples of a target chroma block, and calculating a first difference between the maximum luma value and the minimum luma value; if the first difference is not equal to 0, processing the first difference based on a quantity of significant bits of the first difference and a first preset bit depth to obtain a second difference; and determining, based on a first chroma value, a second chroma value, and the second difference, an intra prediction model parameter corresponding to the target chroma block, and determining prediction information of the target chroma block based on the intra prediction model parameter and luma reconstruction information corresponding to the target chroma block.