Image Encoding Block Partitioning for Boundary Handling
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Solution Overview
Problem
Current 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 use of neighboring block information.
Innovation Solution
The method involves partitioning images into blocks and sub-blocks using quad tree or binary tree partitioning, with recursive division until the boundary is accounted for, and determining partition types based on block sizes and types to optimize compression. This includes encoding and decoding processes that adjust block sizes and partition directions to improve 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 the encoding process is simplified and standardized, 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., 64x64, 32x32, 16x16 pixels) through hierarchical partitioning. This segmentation allows the encoding system to handle images of various sizes by breaking them down into standardized block units, resolving the contradiction between encoding standardization and adaptability to different image dimensions.
Solution Approach 2:
The patent introduces a hierarchical block structure with multiple size levels (64x64, 32x32, 16x16 pixels), adding a dimensional aspect to block processing. This multi-scale block system enables flexible handling of various image sizes while maintaining standardized encoding procedures at each level.
2Productivity
If the image is divided into multiple blocks for encoding, then processing efficiency is improved, but boundary processing between blocks becomes complex and computationally intensive
Solution Approach 1:
The patent applies different processing strategies to different regions: blocks fully contained within the image use standard encoding, while blocks containing boundaries use specialized boundary processing. This local differentiation reduces overall complexity by applying complex operations only where necessary rather than uniformly across all blocks.
Solution Approach 2:
The patent processes only the necessary portions of blocks at boundary regions, rather than applying full processing to entire blocks. By performing partial processing on boundary-affected areas and using simplified methods for other regions, the computational complexity is reduced while maintaining processing efficiency.
3Speed
If larger blocks are used for encoding, then the number of blocks is reduced and processing speed increases, but compression efficiency decreases due to loss of fine detail
Solution Approach 1:
The patent implements hierarchical block partitioning that divides the image into multiple size levels (64x64, 32x32, 16x16 pixels). This segmentation allows the system to use larger blocks for speed-critical regions while applying smaller blocks to detail-rich regions, thereby balancing processing speed with compression efficiency.
Solution Approach 2:
The patent employs dynamic block size selection where the block size is adaptively chosen based on image content characteristics. This dynamic approach allows larger blocks to be used in uniform regions for faster processing while smaller blocks are applied to complex regions to preserve detail, optimizing both speed and compression efficiency.
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.