LFNST Coefficient Scanning Order for CCLM Video Encoding
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Solution Overview
Problem
The applicability of the Low-Frequency Non-Separable Transform (LFNST) technology to the Cross-component Linear Model Prediction (CCLM) mode in H.266/VVC is poor, leading to increased transform processes and reduced encoding efficiency due to the need for extra mapping processing.
Innovation Solution
The proposed solution involves determining the LFNST coefficient scanning order directly according to the CCLM parameter, allowing for flexible selection and eliminating the need for mapping the CCLM mode to a traditional intra prediction mode, thereby improving the applicability of LFNST technology and reducing the transform process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LFNST technology is applied to CCLM mode, then transform processing can be performed, but extra mapping processing is required which increases transform process complexity and reduces encoding efficiency
Solution Approach 1:
The patent extracts the mapping processing step from the transform process by directly determining the LFNST coefficient scanning order based on the CCLM prediction mode index without requiring conversion to traditional intra prediction modes. This removes the unnecessary intermediate mapping step while maintaining the ability to apply LFNST to CCLM mode.
Solution Approach 2:
The patent creates a universal scanning order determination mechanism that works for both traditional intra prediction modes and CCLM mode. By using the CCLM prediction mode index directly to determine the scanning order, the system achieves multi-functionality where the same LFNST process can handle different prediction modes without requiring mode-specific mapping procedures.
2Adaptability or versatility
If mapping processing is performed to convert CCLM mode to traditional intra prediction mode, then LFNST can be applied, but encoding efficiency is reduced due to extra processing steps
Solution Approach 1:
The patent removes the mapping processing step from the encoding workflow by establishing a direct path from CCLM prediction mode index to LFNST coefficient scanning order. This extraction of the unnecessary mapping step directly improves encoding efficiency by reducing the number of processing operations required.
Solution Approach 2:
The patent enables the encoding process to skip the intermediate mapping step by directly determining the scanning order from the CCLM prediction mode index. This skipping of the redundant mapping phase allows the system to rush through the transform process more quickly, thereby improving encoding efficiency.
3Ease of operation
If CCLM mode is mapped to traditional intra prediction mode before determining scanning order, then LFNST processing can proceed, but the transform process becomes more complex
Solution Approach 1:
The patent extracts and eliminates the mapping processing step from the transform workflow. By directly using the CCLM prediction mode index to determine the LFNST coefficient scanning order, the system maintains LFNST processing capability while removing the complexity-introducing mapping operation.
Solution Approach 2:
Instead of converting CCLM mode to traditional intra prediction mode to determine scanning order (the conventional approach), the patent inverts the approach by directly determining the scanning order from the CCLM prediction mode index. This inversion eliminates the unnecessary conversion step while achieving the same operational goal.
Data Source
AI summary
A transform method applied to an encoder includes: determining a prediction mode parameter of a current block; determining a CCLM parameter when the prediction mode parameter indicates that CCLM is used for the current block to determine an intra prediction value; determining the intra prediction value of the current block according to the CCLM parameter and calculating a residual value between the current block and the intra prediction value; performing a first transform on the residual value to obtain a first coefficient matrix; determining an LFNST coefficient scanning order used for the current block according to the CCLM parameter when an LFNST is used for the current block; constructing an input coefficient matrix of the LFNST by using the first coefficient matrix according to the LFNST coefficient scanning order; and performing an LFNST processing on the input coefficient matrix to obtain a transform coefficient matrix of the current block.


