Affine Motion Prediction with Geometry Partitioning for Irregular Objects
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Solution Overview
Problem
Existing video coding standards struggle to efficiently capture the high-order motion characteristics of irregularly shaped moving objects using affine motion compensated prediction, leading to increased bit usage.
Innovation Solution
Introduce geometry partitioning mode (GPM) into affine prediction mode to handle moving objects with irregular shapes, allowing for partitioning of video blocks into geometrically located partitions that can be coded using different or same coding modes with varying parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If affine motion compensated prediction is used for irregularly shaped objects, then motion characteristics can be captured, but bit usage increases due to inefficiency
Solution Approach 1:
The video block is divided into multiple partitions based on geometric characteristics (e.g., triangular, quadrangular partitions). Each partition is then processed with appropriate motion prediction modes, allowing efficient representation of irregularly shaped objects while reducing overall bit usage compared to applying affine prediction to the entire block.
Solution Approach 2:
Different motion prediction modes are applied to different partitions within the same video block. For example, affine prediction is applied only to specific partitions where high-order motion characteristics are present, while other partitions use simpler prediction modes, optimizing the balance between accuracy and bit rate.
2Productivity
If geometry partitioning is applied to video blocks, then coding efficiency improves, but processing complexity increases
Solution Approach 1:
The video block is segmented into geometric partitions (triangular, quadrangular, or other shapes) that follow the boundaries of moving objects. This segmentation enables more accurate motion representation and improves coding efficiency by reducing residual errors and enabling better compression.
Solution Approach 2:
The system dynamically selects the partitioning mode and motion prediction mode for each partition based on the characteristics of the video content. This allows the encoder to adapt to different scenarios, applying complex geometry partitioning only when beneficial, thus managing processing complexity while maintaining coding efficiency.
Data Source
AI summary
Implementations of the disclosure provide systems and methods for motion compensation prediction. The method may include performing, by a video processor, a geometry partition on a video block of a video frame from a video to obtain a first partition and a second partition. The method may further include applying, by the video processor, a first motion prediction mode to the first partition and a second motion prediction mode to the second partition. At least one of the first motion prediction mode or the second motion prediction mode is an affine motion prediction mode.