Template Matching for Accurate Video Block Partitioning
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Solution Overview
Problem
Existing video coding technologies face inefficiencies in deriving optimal partitioning patterns for video blocks, leading to suboptimal compression and reconstruction quality.
Innovation Solution
The method involves template matching (TM) to determine a reference block from candidate blocks based on TM costs, classify samples into classes, and derive a partitioning pattern for the current block, using techniques such as binary image segmentation and edge detection to enhance the reconstruction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If template matching based partitioning pattern derivation is used, then partitioning accuracy and reconstruction quality are improved, but computational complexity increases
Solution Approach 1:
The method segments the current block into multiple candidate reference blocks and processes each candidate separately through template matching. This segmentation allows the system to evaluate multiple partitioning patterns in parallel, improving partitioning accuracy while managing computational complexity through structured processing of individual candidates.
Solution Approach 2:
The system performs preliminary actions by pre-defining a set of candidate reference blocks and their corresponding template matching costs before processing the current block. This preliminary preparation enables faster decision-making during partitioning pattern derivation, reducing the computational burden during the actual partitioning process.
2Measurement precision
If multiple candidate reference blocks are evaluated through template matching, then partitioning pattern accuracy is improved, but processing time increases
Solution Approach 1:
The method incorporates feedback mechanisms where template matching costs from candidate reference blocks are used to guide the selection of the optimal partitioning pattern. This feedback loop allows the system to efficiently evaluate multiple candidates and converge on the best partitioning pattern, balancing accuracy with processing time through iterative refinement.
Solution Approach 2:
The system dynamically adjusts parameters such as template matching thresholds and candidate block selections based on the specific characteristics of the current block. By adapting these parameters to the content being processed, the system maintains high partitioning pattern accuracy while optimizing processing time for different video scenarios.
Data Source
AI summary
A reference block is determined from a plurality of candidate reference blocks for a current block in a current picture based on template matching (TM) costs of the plurality of candidate reference blocks. The TM costs indicate differences between a template of the current block and respective reference templates of the plurality of candidate reference blocks. Samples of the determined reference block are classified into a plurality of classes of samples. A partitioning pattern of the current block is derived based on the determined reference block from a predetermined plurality of partitioning patterns. The derived partitioning pattern indicates a plurality of partitions of the current block. Each of the plurality of classes of the samples of the determined reference block corresponds to a respective partition of the plurality of partitions of the current block. The current block is reconstructed based on the derived partitioning pattern of the current block.