Geometric Partition Refinement for Adaptive Video Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video encoding and decoding technologies face challenges in efficiently partitioning video sequences into blocks for effective prediction and compression, particularly in handling complex content like screen content, leading to suboptimal compression efficiency and quality.
Innovation Solution
Implementing geometric partitioning refinement techniques, including quadtree+multi-type tree partitioning and refined partition edges, to enhance block partitioning strategies in video coding, allowing for more adaptive and efficient block partitioning based on content homogeneity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional block partitioning is used for video encoding, then the encoding process is simple, but the compression efficiency and video quality are suboptimal for complex content
Solution Approach 1:
The video sequence is divided into multiple blocks using quadtree partitioning, where each block can be further subdivided into smaller blocks. This hierarchical segmentation allows the encoder to adaptively choose the appropriate block size for different regions, improving compression efficiency by matching the partitioning structure to the actual content characteristics while maintaining manageable complexity through systematic division.
Solution Approach 2:
Different partitioning strategies are applied to different regions of the video sequence based on local content characteristics. Smooth regions use larger blocks for better compression, while complex regions with edges or textures use smaller blocks to preserve detail. This local adaptation optimizes the balance between compression efficiency and quality for each specific region without requiring complex global processing.
2Measurement precision
If block partitioning is refined to improve prediction accuracy, then video quality improves, but the computational complexity increases
Solution Approach 1:
The encoder performs preliminary analysis of the video content to identify regions that benefit from refined partitioning. By pre-identifying areas with edges, textures, or motion boundaries, the system can apply computationally intensive refinement operations only where necessary, rather than uniformly across the entire sequence. This preliminary action reduces overall computational complexity while maintaining high prediction accuracy in critical regions.
Solution Approach 2:
Instead of applying full refinement to all blocks, the system applies partitioning refinement selectively to only those blocks where it provides meaningful improvement. This partial action approach focuses computational resources on blocks that require enhanced prediction accuracy, accepting that some blocks will use standard partitioning. The result is a balance between overall video quality and computational complexity.
Data Source
AI summary
In some embodiments, a decoder may receive, from a bit stream, a residual of a block and an indication that the residual was determined based on a refined partition edge. The decoder may determine the refined partition edge based on the indication and reference samples neighboring the block. The decoder may determine based on at least one prediction mode, a prediction for two parts of the block defined by the refined partition edge. The decoder may determine a reconstruction for the block based on the residual and the prediction of the block.