Cross-Component Geometric Partitioning for Irregular Video Objects
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Solution Overview
Problem
Existing video coding technologies use partitioning modes that are sub-optimal for irregularly shaped video objects, leading to inefficient motion predictions and subpar encoding and decoding quality.
Innovation Solution
Implement geometric partitioning modes that allow for more flexible partitioning boundaries beyond straight lines, using geometric partitions to better represent the shape of video objects, improving motion prediction accuracy and encoding quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional partitioning modes with straight line boundaries are used, then the implementation is simple, but the accuracy of motion prediction and encoding quality deteriorate for irregularly shaped video objects
Solution Approach 1:
The patent applies curvature by replacing straight line partition boundaries with curved geometric boundaries (arcs, circles, ellipses) to better fit irregularly shaped video objects. This allows the partitioning to adapt to the actual shape of objects in the video, improving motion prediction accuracy while maintaining manageable complexity through standardized curved geometry types.
Solution Approach 2:
The patent introduces dynamic partitioning modes where the geometric partition parameters (such as center coordinates, radius, orientation) can be adjusted based on the content characteristics. This allows the partitioning to adapt dynamically to different video scenes and object shapes, enhancing prediction accuracy without requiring completely complex custom partitioning for each scenario.
2Measurement precision
If geometric partitioning modes with flexible boundaries are implemented, then the representation of video object shapes improves, but the complexity of partition derivation and processing increases
Solution Approach 1:
The patent segments the complex task of partition derivation into multiple manageable steps: first identifying the geometric partition type, then calculating partition parameters based on predefined models, and finally refining boundaries if necessary. This segmentation reduces the overall complexity by breaking down the derivation process into standardized, easier-to-implement operations.
Solution Approach 2:
The patent uses parameter changes by deriving partition boundaries through mathematical transformations of geometric parameters (such as converting between different coordinate systems or adjusting transformation matrices). This approach simplifies the derivation process by working with manageable parameter spaces rather than directly computing complex boundary shapes.
3Productivity
If cross-component geometric partition derivation is applied, then the encoding efficiency and quality improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies universality by deriving geometric partitions for one color component (such as luma) and then reusing these partitions for other color components (such as chroma). This multi-functional approach improves encoding efficiency by avoiding redundant partition derivation operations, while the computational cost is managed through the fact that chroma components typically require less processing anyway.
Data Source
AI summary
An example method of video decoding includes obtaining a video bitstream comprising a plurality of blocks including a block having a first color component and a second color component, where the block is coded in a geometric partition mode comprising a first geometric partition and a second geometric partition. The method further includes reconstructing samples in a first geometric partition of the first color component using the first geometric partition, and deriving the second geometric partition for the second color component based on the reconstructed samples of the first color component. The method also includes reconstructing samples of the second color component using the second geometric partition, and decoding the block based on the reconstructed samples of the first color component and the reconstructed samples of the second color component.