Video Encoding Quadtree-Plus-Binary-Tree Partitioning
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Solution Overview
Problem
Current video compression technologies face inefficiencies in encoding and decoding due to redundant data not being adequately addressed, particularly in the partitioning of image blocks, leading to increased complexity and reduced performance.
Innovation Solution
The implementation of a quadtree-plus-binary-tree partition pattern that constrains the partition pattern of subimage blocks to reduce redundancy, allowing for flexible and efficient encoding and decoding by limiting the partition methods used, thereby eliminating redundancy and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional quadtree partition pattern is used for image blocks, then encoding and decoding can be performed, but redundancy in partition patterns increases complexity and reduces efficiency
Solution Approach 1:
The patent segments the image block partitioning process into two distinct phases: first applying quadtree partitioning to divide the image into quadrants, then applying binary tree partitioning to further subdivide selected quadrants. This segmentation of the partitioning process allows for more flexible and efficient encoding by reducing redundancy in partition patterns, directly resolving the contradiction between encoding efficiency and partition pattern complexity
Solution Approach 2:
The patent introduces a dynamic constraint mechanism that adapts the partition pattern based on image characteristics. The constraint subimage processing mode dynamically determines whether to apply further binary tree partitioning to each quadrant, allowing the system to adjust the partition depth and pattern according to actual image content, thereby improving encoding efficiency while controlling complexity
2Productivity
If constraint subimage processing mode is applied to process first and second subimage blocks, then redundancy is reduced and encoding efficiency is improved, but processing steps increase
Solution Approach 1:
The patent applies local quality by treating the first and second subimage blocks differently based on their specific characteristics and the constraint subimage processing mode. Each subimage block is processed with appropriate constraints that optimize redundancy reduction for that specific region, allowing efficient encoding while managing processing complexity through localized adaptation rather than uniform processing
Solution Approach 2:
The patent performs preliminary partitioning of the image block into four quadrants using quadtree partitioning before applying the constraint subimage processing mode. This preliminary action organizes the data structure in advance, enabling the subsequent binary tree partitioning and constraint application to proceed more efficiently, thus improving overall encoding efficiency while controlling the increase in processing steps
Data Source
AI summary
A decoding method includes: parsing a data stream, and if partitioning an image block with a size of 2N×2N using a quadtree partition pattern is allowed, processing a 2N×N first subimage block and a 2N×N second subimage block or an N×2N first subimage block and an N×2N second subimage block in a constraint subimage processing mode, wherein an image block partition pattern obtained for the partitioned second subimage block and the partitioned first subimage block is different from an image block partition pattern obtained after the 2N×2N image block is partitioned using the quadtree partition pattern, where the 2N×N first subimage block and the 2N×N second subimage block or the N×2N first subimage block and the N×2N second subimage block are obtained by partitioning the image block with the size of 2N×2N.