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
1Productivity
If image encoding is performed on a per block basis, then compression efficiency is improved, but it becomes difficult to process images where the size is not a multiple of the block size
Solution Approach 1:
The image is divided into multiple blocks of a predetermined size for encoding. When the image size is not a multiple of the block size, the image is segmented into regular blocks that can be processed efficiently and a remaining area that handles the residual pixels, allowing per-block compression while adapting to arbitrary image dimensions.
Solution Approach 2:
The problem is solved by introducing a dimensional distinction between the main image area (processed in regular blocks) and the remaining area (handling boundary cases). This dimensional separation allows the encoding system to maintain fixed block sizes for compression efficiency while accommodating variable image sizes through the remaining area concept.
2Ease of operation
If the image size is adjusted to be a multiple of block size, then per block encoding becomes possible, but image processing complexity increases
Solution Approach 1:
The remaining area containing pixels that don't fit into complete blocks is extracted and identified separately. This extraction allows the main encoding process to operate on regular blocks with simple arithmetic, while the remaining area is handled as a special case, reducing overall processing complexity compared to forcing all pixels into padded blocks.
Solution Approach 2:
The encoding system automatically determines the block size and calculates the remaining area based on the input image dimensions. This self-service mechanism eliminates the need for manual image resizing or complex padding logic, as the system adapts its block structure to fit the image size naturally.
3Productivity
If recursive partitioning is performed on blocks containing boundaries, then boundary processing efficiency is improved, but encoding complexity increases
Solution Approach 1:
The partitioning structure is made dynamic by allowing recursive subdivision only of blocks that contain image boundaries. Regular blocks are encoded at a fixed level, while boundary blocks are dynamically partitioned into sub-blocks based on their position. This dynamic adaptation improves boundary processing efficiency without applying complex recursion to all blocks, balancing complexity and performance.
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.