Image Encoding with Recursive Boundary Block Splitting
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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, with partition information derived from bitstreams.
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 image can be segmented into a first block and a second block with different dimensions, allowing flexible adaptation to various image sizes while maintaining block-based encoding simplicity.
Solution Approach 2:
The block partitioning structure is made dynamic and adaptive rather than fixed. The encoding system can dynamically adjust block sizes and configurations based on the actual image dimensions and content characteristics, enabling both simplicity and adaptability.
2Device complexity
If the entire image is encoded as a single block, then encoding process is simple, but compression efficiency deteriorates due to inability to exploit local variations
Solution Approach 1:
The image is segmented into multiple blocks of different sizes based on local content characteristics. This allows the encoding process to exploit local variations and correlations within each block, improving compression efficiency while maintaining manageable complexity through hierarchical partitioning.
Solution Approach 2:
Different blocks are encoded with different qualities and methods based on their local characteristics. The encoding process adapts to local variations in the image, applying appropriate compression strategies to different regions, thereby improving overall compression efficiency.
3Productivity
If image boundaries are not specially processed, then encoding is straightforward, but boundary artifacts and poor reconstruction quality occur
Solution Approach 1:
Boundary processing is performed in advance during the partitioning stage. The method preliminarily identifies and handles boundary blocks with special processing rules, preventing boundary artifacts from occurring during reconstruction while maintaining encoding 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.