Geometric Merge Mode Subblock Partitioning for Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in achieving high compression ratios with minimal sacrifice in picture quality, particularly in streaming and storage applications where bandwidth and memory resources are limited.
Innovation Solution
The proposed method involves a computer-implemented process for inter-prediction in video coding, which includes obtaining parameters for a geometric merge (GEO) inter prediction mode, defining a subblock within a current coding block, determining allowed split directions for a triangle partitioning mode (TPM), and using TPM to obtain predicted samples for the current block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional inter prediction methods are used, then picture quality can be maintained, but the compression ratio is reduced and data amount increases
Solution Approach 1:
The current block is divided into two subblocks (first and second subblocks), allowing different prediction modes to be applied to different regions. This segmentation enables more efficient compression by matching the complexity of prediction to the actual content variation in each region, reducing overall data amount while maintaining picture quality.
Solution Approach 2:
Different prediction precision levels are applied to different subblocks based on their local characteristics. The first subblock uses one prediction mode while the second subblock uses another, allowing each region to be predicted with the appropriate level of detail needed for its specific content, thereby reducing total data requirements without sacrificing overall picture quality.
2Manufacturing precision
If complex prediction modes are used, then picture quality is improved, but device complexity and processing overhead increase
Solution Approach 1:
The block is segmented into subblocks that can be processed independently with simpler prediction modes. By dividing the current block into first and second subblocks with different prediction approaches, the processing complexity for each subblock is reduced compared to applying a single complex mode to the entire block, while still achieving good picture quality through localized adaptation.
3Ease of operation
If uniform prediction is applied to the entire block, then processing is simplified, but adaptation to local variations is reduced
Solution Approach 1:
The current block is divided into first and second subblocks, each capable of using different prediction modes. This segmentation maintains relative processing simplicity while enabling local adaptation to different content characteristics in different regions of the block, achieving a balance between ease of operation and adaptability.
Solution Approach 2:
Different prediction modes are assigned to different subblocks based on their local content characteristics. The first subblock may use a prediction mode suited for its content while the second subblock uses a different mode, providing local adaptation without requiring complex processing across the entire block.
Data Source
AI summary
A method includes: obtaining one or more parameters of a geometric merge (GEO) inter prediction mode for a current coding block; defining a size of a subblock that is smaller than a size of the current coding block; specifying a position of the subblock within the current coding block, so that either two or three sides of the subblock coincide with sides of the current coding block; determining one or more allowed split directions of a triangle partitioning mode (TPM) for the subblock depending on which of the sides of the subblock coincide with the sides of the current coding block; and obtaining one or more predicted samples for the current coding block by using the TPM partitioning performed for the subblock in accordance with the size of the subblock, the position of the subblock within the current coding block and a TPM split direction of the subblock.