Geometric Partition Weighting for More Accurate Video Block Prediction
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently utilizing spatial and temporal redundancies in video data compression, particularly in predicting sample values within a current block using motion compensation and intra prediction.
Innovation Solution
Implementing a geometric partitioning mode (GPM) that uses a nonlinear polynomial model to determine weights for weighted averaging of predictions based on neighboring samples, allowing for improved prediction accuracy and efficiency in video encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction modes (intra prediction and motion compensation) are used, then video compression can be achieved, but prediction accuracy and compression efficiency are insufficient
Solution Approach 1:
The patent combines multiple prediction modes (intra prediction and motion compensation prediction) into a unified geometric partitioning mode. By merging these prediction approaches and using geometric partitioning to divide the current block into multiple regions, the system achieves both high prediction accuracy and efficient compression by leveraging the strengths of both prediction methods simultaneously.
Solution Approach 2:
The geometric partitioning mode divides the current block into multiple geometric partitions with different partitioning directions (vertical, horizontal, diagonal). Each partition can be predicted using different prediction modes optimized for its specific characteristics. This local differentiation allows the system to achieve high prediction accuracy in each region while maintaining overall compression efficiency through selective prediction mode application.
2Measurement precision
If geometric partitioning mode with nonlinear polynomial model is applied, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent uses a nonlinear polynomial model with specific parameters (coefficients a, b, c, d, e) to calculate geometric partition weights. By changing the parameters from simple linear values to nonlinear polynomial coefficients, the system achieves more accurate prediction of sample values within geometric partitions. The polynomial model allows flexible adjustment of weight distribution across different partition regions, improving prediction accuracy while keeping the computational structure manageable through standardized parameter sets.
Data Source
AI summary
An apparatus for video decoding includes processing circuitry configured to receive coded information indicating that a current block is coded with a geometric partitioning mode (GPM) using a first prediction mode and a second prediction mode. Coefficients of a nonlinear polynomial model that indicates a weight w0 of a first prediction obtained from the first prediction mode are determined based on a current template and reference templates. Each reference template is obtained based on the first prediction mode, the second prediction mode, and a respective candidate nonlinear polynomial model. The nonlinear polynomial model depends on at least one of x and y. (x, y) indicates a sample position in the current block. The current block is reconstructed based on a weighted average of the first prediction and a second prediction of the current block obtained using the second prediction mode according to the weight w0.