Image Encoding with Recursive Boundary Sub-Block Partitioning
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Solution Overview
Problem
Existing image encoding/decoding technologies face challenges in efficiently processing image boundaries and adjusting image sizes on a per block basis, leading to difficulties in compression efficiency and effective utilization of neighboring block information.
Innovation Solution
The method involves partitioning current images into blocks and sub-blocks, using quad tree or binary tree partitioning based on boundary conditions, and determining partition directions and types to optimize image encoding/decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image encoding is performed on a per block basis with fixed block sizes, then encoding simplicity is maintained, but images with sizes not matching block dimensions cannot be properly encoded
Solution Approach 1:
The image is divided into multiple blocks of different sizes through recursive partitioning. The encoding apparatus segments the image into blocks that can be uniformly processed while adapting to various image dimensions, thus maintaining encoding simplicity while improving size adaptability.
Solution Approach 2:
The block partitioning structure is made dynamic through recursive division. Instead of using fixed block sizes, the system dynamically creates blocks of varying sizes by recursively partitioning the image, allowing the encoding process to adapt to different image dimensions while maintaining a consistent processing framework.
2Manufacturing precision
If recursive partitioning is performed on blocks containing image boundaries, then boundary processing accuracy is improved, but encoding complexity increases
Solution Approach 1:
Different partitioning strategies are applied to different regions of the image based on their characteristics. Blocks containing image boundaries undergo recursive partitioning to achieve higher boundary processing accuracy, while other blocks use standard partitioning, thus improving precision without uniformly increasing complexity across the entire image.
Solution Approach 2:
Recursive partitioning is applied selectively only to blocks that contain image boundaries rather than to all blocks. This partial application of the complex operation reduces the overall encoding complexity while still achieving improved boundary processing accuracy where it is most needed.
3Productivity
If image boundaries are processed with fine-grained partitioning, then compression efficiency is improved, but processing time increases
Solution Approach 1:
Fine-grained partitioning is applied locally only to regions containing image boundaries where it provides compression benefits, while other regions use coarser partitioning. This approach improves compression efficiency at boundaries without unnecessarily increasing processing time across the entire image.
Solution Approach 2:
The computationally intensive recursive partitioning is performed partially only on boundary-containing blocks rather than on all blocks. This reduces the total processing time while still achieving improved compression efficiency in the critical boundary regions.
Data Source
AI summary
The present invention provides an image encoding method and an image decoding method. The image encoding method of the present invention comprises: a first dividing step of dividing a current image into a plurality of blocks; and a second dividing step of dividing, into a plurality of sub blocks, a block, which is to be divided and includes a boundary of the current image, among the plurality of blocks, wherein the second dividing step is recursively performed by setting a sub block including the boundary of the current images as the block to be divided, until the sub block including the boundary of the current image does not exist among the sub blocks.