Picture-Boundary Block Partitioning for Higher Image Coding Efficiency
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Solution Overview
Problem
Conventional image encoding/decoding methods face limitations in improving coding efficiency, particularly with high-resolution and high-quality images, leading to increased data transmission and storage costs due to the use of quad-tree block structures.
Innovation Solution
The method employs various block partitioning schemes, including quad tree, horizontal and vertical binary tree, and ternary tree partitioning, based on the current block's boundaries and size relative to predefined maximum transform blocks, to determine optimal partitioning for improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If quad-tree block structure is used for image encoding/decoding, then the encoding/decoding process is simple, but coding efficiency is limited and data volume is large
Solution Approach 1:
The patent divides the image into multiple blocks and applies different partitioning schemes (quad-tree, binary-tree, ternary-tree) to different blocks based on their characteristics. This segmentation allows the system to optimize coding efficiency for each block while maintaining overall processability, resolving the contradiction between simple processing and high coding efficiency.
Solution Approach 2:
The patent dynamically selects partitioning schemes based on block characteristics such as whether the block is at a picture boundary and its size relative to maximum transform blocks. This dynamic adaptation enables the system to achieve high coding efficiency without requiring overly complex predetermined structures, as the complexity is introduced only where necessary.
2Adaptability or versatility
If quad-tree block structure is used for image encoding/decoding, then the block structure is uniform, but coding efficiency cannot be improved for high-resolution images
Solution Approach 1:
The patent introduces asymmetric partitioning schemes (binary-tree and ternary-tree partitions) in addition to the symmetric quad-tree structure. This allows blocks to be divided into unequal parts based on their specific characteristics, particularly for blocks at picture boundaries, thereby improving coding efficiency without sacrificing the simplicity of the base quad-tree structure.
Solution Approach 2:
The patent applies different partitioning schemes to different local regions (blocks) based on their specific characteristics. Blocks at picture boundaries use binary-tree or ternary-tree partitioning, while other blocks use quad-tree partitioning. This localized optimization improves overall coding efficiency while maintaining uniform processing for the majority of blocks.
3Productivity
If various block partitioning schemes are used to improve coding efficiency, then compression efficiency is enhanced, but device complexity increases
Solution Approach 1:
The patent changes the partitioning parameters (scheme type, partition direction, number of partitions) based on specific block characteristics such as picture boundary location and block size. This parameter-based selection allows the system to achieve high compression efficiency through targeted complexity rather than uniform high complexity across all blocks, resolving the contradiction between compression efficiency and device complexity.
Data Source
AI summary
In this specification, a image decoding method is disclosed. The image decoding method of the present invention comprises, decoding block partition information of a current block included in a current picture, determining a partitioning scheme for the current block according to the block partition information and partitioning the current block using the partitioning scheme determined, wherein the partitioning scheme is determined according to whether the current block includes a boundary of the current picture.