Recursive Boundary Block Partitioning in Image Encoding
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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 the encoding process becomes systematic and manageable, 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 predetermined sizes (e.g., 16x16, 32x32, 64x64 pixels). Each block can be independently encoded, allowing systematic processing while accommodating different image dimensions through hierarchical segmentation into different block size levels.
Solution Approach 2:
The block size is made dynamic rather than fixed. The encoding system can adaptively select different block sizes (16x16, 32x32, 64x64) based on the image content characteristics and dimensions, allowing the same encoding framework to handle various image sizes effectively.
2Device complexity
If larger blocks are used for encoding, then the encoding complexity per block decreases, but the precision of boundary processing deteriorates
Solution Approach 1:
The image is segmented into multiple blocks of predetermined sizes. By dividing the image into smaller manageable blocks, boundary processing precision is improved while maintaining systematic encoding. The segmentation allows detailed processing at block boundaries without overwhelming complexity.
Solution Approach 2:
Different encoding strategies are applied to different regions: boundary blocks receive special attention with refined processing methods, while internal blocks use standard encoding. This local quality approach ensures high boundary processing precision without increasing overall complexity uniformly across the entire image.
3Manufacturing precision
If recursive partitioning is performed until no sub-block includes the boundary, then boundary processing becomes thorough, but the processing time increases
Solution Approach 1:
Instead of recursively partitioning until absolutely necessary, the method performs partitioning to a predetermined depth or until blocks reach a minimum size threshold. This partial action approach achieves sufficient boundary processing thoroughness without the excessive processing time of complete recursive partitioning.
Solution Approach 2:
The image is pre-divided into blocks of predetermined sizes before detailed encoding begins. This preliminary segmentation establishes a structured framework that facilitates efficient boundary processing without requiring extensive recursive partitioning during the main encoding process.
4Ease of manufacture
If padding is applied to make image size a multiple of block size, then per-block encoding becomes feasible, but the image data volume increases
Solution Approach 1:
The image is segmented into blocks that fit within the original image dimensions without requiring external padding. By using hierarchical block sizes (16x16, 32x32, 64x64), the segmentation adapts to the original image size, making per-block encoding feasible without adding extra data.
Solution Approach 2:
The block size parameter is made variable rather than fixed. By allowing different block sizes (16x16, 32x32, 64x64) and dynamically selecting appropriate sizes, the system achieves per-block encoding feasibility without uniform padding, thereby avoiding unnecessary data volume increase.
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.