Image Block Partitioning for Boundary-Aware Video 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 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 is first divided into a plurality of blocks, and then a partition target block including a boundary is further divided into sub-blocks. This segmentation allows the system to handle images of various sizes by creating appropriate block structures rather than requiring fixed block sizes.
Solution Approach 2:
The block partitioning structure is made dynamic and adaptive rather than fixed. The partitioning process recursively divides blocks based on boundary conditions, allowing the block size and structure to adapt to the specific image dimensions and content requirements. This dynamic approach resolves the contradiction between encoding simplicity and image size adaptability.
2Ease of operation
If boundary blocks are processed as complete blocks, then processing simplicity is maintained, but encoding efficiency is reduced due to wasted encoding resources in non-image areas
Solution Approach 1:
Boundary blocks are segmented into multiple sub-blocks through recursive partitioning. This segmentation allows the encoder to process only the actual image content areas within boundary blocks, excluding non-image or padding areas from encoding operations. This resolves the contradiction by maintaining simple block-based processing while improving encoding efficiency through selective sub-block processing.
Solution Approach 2:
Different processing approaches are applied to different regions within boundary blocks. The partitioning identifies and separates actual image content from non-image areas, allowing encoding operations to be applied selectively to relevant regions. This local differentiation improves encoding efficiency without significantly complicating the overall processing framework.
3Stability of the object's composition
If uniform partitioning is applied to all blocks, then processing consistency is maintained, but compression efficiency is reduced due to inability to adapt to local image characteristics
Solution Approach 1:
The partitioning process applies different division strategies to different blocks based on their specific characteristics, particularly whether they contain image boundaries. This local adaptation allows the system to maintain processing consistency through a unified recursive framework while achieving better compression efficiency by tailoring partitioning to local image content requirements.
Solution Approach 2:
The partitioning structure transitions from static uniform division to dynamic adaptive division. The recursive partitioning algorithm dynamically determines block divisions based on boundary conditions and image content, allowing the system to maintain consistency in its adaptive approach while achieving superior compression efficiency compared to fixed uniform partitioning.
4Manufacturing precision
If recursive partitioning is performed on all blocks, then boundary processing accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
Recursive partitioning is applied selectively only to partition target blocks that contain image boundaries, rather than uniformly to all blocks. This segmentation of the partitioning operation maintains high boundary processing accuracy where needed while avoiding unnecessary computational complexity in blocks that do not require such detailed processing.
Solution Approach 2:
The patent applies recursive partitioning partially - only to blocks that contain boundaries - rather than excessively applying it to all blocks. This partial action achieves the necessary boundary processing accuracy while significantly reducing overall computational complexity compared to universal recursive partitioning.
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.