Quinary Video Block Partitioning for High-Resolution Compression
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Solution Overview
Problem
Existing video coding standards face challenges in efficiently handling higher resolution video data, particularly in terms of bandwidth usage and compression efficiency, as they lack flexibility in block partitioning methods.
Innovation Solution
The introduction of quinary tree partitioning in video coding, which allows for more flexible block partitioning beyond traditional quad-tree and binary-tree structures, enabling improved compression efficiency and runtime performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional quad-tree or binary-tree partitioning is used, then the video coding standard maintains simplicity and compatibility, but compression efficiency and adaptability to high-resolution video are insufficient
Solution Approach 1:
The video block is segmented into five sub-blocks instead of the traditional four (quad-tree) or two (binary-tree) partitions. This quinary partitioning method divides the current video block into five separate regions, allowing more flexible adaptation to different video content characteristics and improving compression efficiency for high-resolution video while maintaining a relatively simple partitioning structure.
2Productivity
If more flexible partitioning methods are introduced, then compression efficiency improves, but processing complexity and computational load increase
Solution Approach 1:
The quinary partitioning method applies different partitioning strategies to different regions of the video block based on local content characteristics. By dividing the block into five regions, the encoder can adaptively select partitioning patterns that best suit local variations in video content, improving compression efficiency without requiring complex global processing.
Solution Approach 2:
The partitioning structure is made dynamic by allowing the video coder to selectively apply quinary partitioning based on video content characteristics, resolution, and other factors. This dynamic approach enables the system to switch between different partitioning modes (quad-tree, binary-tree, or quinary) to optimize compression efficiency while controlling processing complexity based on actual needs.
Data Source
AI summary
Devices, systems, and methods for video processing are described. In a representative aspect, a method for video processing is disclosed. The method includes determining, for a current video block, whether a first partition mode is applicable to the current video block in responsive to at least one condition. The current video block is split into M sub-blocks in the first partition mode, and M>4. The method also includes performing a conversion for the current video block based on the determination.