Recursive Image Partitioning for Boundary-Aware Block 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, including methods for deriving partition information without explicit encoding.
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 (quad-tree or binary-tree structures). Each block can be independently encoded, allowing the system to handle images of various dimensions by creating an appropriate block hierarchy that adapts to the image size while maintaining per-block encoding simplicity.
Solution Approach 2:
The block partitioning structure is made dynamic and adaptive rather than fixed. The partitioning tree can be constructed recursively with different division patterns (quad-tree for 4 subdivisions, binary-tree for 2 subdivisions) depending on image characteristics and size requirements, enabling the encoding system to adapt to different image dimensions while keeping the encoding process itself simple and modular.
2Productivity
If recursive partitioning is performed on blocks containing image boundaries, then boundary processing efficiency is improved, but the partitioning complexity increases
Solution Approach 1:
The partitioning process is performed recursively and systematically before encoding, with special attention to ensuring that blocks containing image boundaries are properly identified and processed. By pre-establishing the partitioning structure and identifying boundary-containing blocks in advance, the actual boundary processing during encoding becomes more efficient, while the recursive nature provides a systematic approach that manages complexity.
Solution Approach 2:
Different partitioning strategies are applied locally depending on the block's characteristics. Blocks containing image boundaries may receive different treatment (such as forced partitioning or specific partition directions) compared to interior blocks. This localized approach optimizes boundary processing efficiency without requiring complete redesign of the overall partitioning system, thereby managing complexity while improving boundary handling.
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.