Geometric Partition Intra Prediction for Mixed-Direction Blocks
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Solution Overview
Problem
Existing video encoding and decoding technologies struggle to efficiently handle blocks with varying directional features within a frame, as they often rely on single angular or non-angular modes that inadequately describe regions with different directionalities, leading to inefficiencies in compression and prediction accuracy.
Innovation Solution
Implementing a method and apparatus for intra prediction that splits a block into multiple partitions using a straight line, applies different intra prediction modes to each partition, and adjusts sample values along the partition boundary with adaptive weights, specifically utilizing geometric partitioning for improved alignment with object boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single intra prediction mode is used for the entire block, then the device complexity is reduced, but the prediction accuracy deteriorates when the block contains regions with different directional features
Solution Approach 1:
The block is divided into multiple partitions using a straight line split, where each partition can be independently predicted using different intra prediction modes. This segmentation allows the encoder to adapt to different directional features in different regions of the block, improving prediction accuracy without requiring complex per-pixel analysis.
Solution Approach 2:
Different intra prediction modes are applied to different partitions of the block based on their local directional characteristics. This local quality approach ensures that each region is predicted using the most appropriate mode for its specific features, rather than applying a uniform mode across the entire block.
2Measurement precision
If different intra prediction modes are applied to different regions, then the prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The block is divided into multiple partitions using a straight line split, where each partition can be independently predicted using different intra prediction modes. This segmentation allows the encoder to adapt to different directional features in different regions of the block, improving prediction accuracy without requiring complex per-pixel analysis.
Solution Approach 2:
Different intra prediction modes are applied to different partitions of the block based on their local directional characteristics. This local quality approach ensures that each region is predicted using the most appropriate mode for its specific features, rather than applying a uniform mode across the entire block.
3Productivity
If geometric partitioning is used to align with object boundaries, then the compression efficiency is improved, but the device complexity increases
Solution Approach 1:
The block is divided into multiple partitions using a straight line split, where each partition can be independently predicted using different intra prediction modes. This segmentation allows the encoder to adapt to different directional features in different regions of the block, improving prediction accuracy without requiring complex per-pixel analysis.
Solution Approach 2:
Different intra prediction modes are applied to different partitions of the block based on their local directional characteristics. This local quality approach ensures that each region is predicted using the most appropriate mode for its specific features, rather than applying a uniform mode across the entire block.
Data Source
AI summary
In one implementation, when coding a block in the intra prediction mode, the block can be split into two sub-partitions by a geometrically located straight line. Each geometric partition within the CU is intra predicted using its own intra mode with its available reference sample. One sub-partition copies and uses the intra prediction mode from the parent block, and another sub-partition uses another implicit or explicit signaled intra prediction mode. After predicting geometric partition, the sample values along the split boundary are adjusted using a blending process with adaptive weights. The geometric partition based intra prediction could be applied for one angular intra prediction mode, or only for one negative-directional intra prediction mode, or only for one specific intra prediction mode (e.g., mode 34). The transform selection or other intra coding tools (i.e., intra sub-partition) can be adapted for the geometric partition based intra prediction.