Image Encoding and Decoding With Recursive Boundary 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 recursively partitioning image blocks and sub-blocks, using quad tree and binary tree partitioning based on block boundaries, and adjusting partitioning methods based on threshold values and block types to optimize image size and compression efficiency.
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 it becomes difficult to handle images whose sizes are not multiples of the block size
Solution Approach 1:
The image is divided into multiple blocks of a predetermined size (e.g., 64x64 pixels). Each block is independently encoded, which simplifies the encoding process while allowing the system to handle images of various sizes through proper block segmentation and padding strategies.
2Productivity
If the image size is adjusted to be a multiple of block size, then per-block encoding becomes feasible, but processing complexity increases due to additional boundary handling
Solution Approach 1:
Different processing approaches are applied to different regions of the image. Boundary blocks receive special handling with additional padding operations, while internal blocks follow the standard encoding process. This localized approach manages complexity by only applying additional processing where necessary.
3Loss of energy
If neighboring block information is utilized for compression, then compression efficiency improves, but the complexity of information utilization increases
Solution Approach 1:
The encoding process automatically utilizes information from already-encoded neighboring blocks to improve compression of current blocks. The system leverages spatial redundancy by referencing adjacent block data, achieving better compression ratios without requiring complex external processing or manual intervention.
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.