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 size adjustment and boundary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
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 encoding apparatus segments the image into blocks that can be evenly divided, allowing flexible adaptation to various image dimensions while maintaining systematic encoding processes.
Solution Approach 2:
The block size is made dynamic rather than fixed. The encoding apparatus automatically adjusts block dimensions based on the image size and boundary conditions, enabling the system to adapt to different image formats while preserving encoding efficiency.
2Adaptability or versatility
If boundary blocks are processed with special padding operations, then image boundary processing capability is improved, but processing complexity increases
Solution Approach 1:
Different processing strategies are applied to different regions of the image. Boundary blocks receive specialized padding operations while internal blocks use standard processing, allowing the system to handle boundary conditions effectively without uniformly increasing complexity across the entire image.
Solution Approach 2:
Padding operations are performed in advance on boundary blocks before the main encoding process. By preparing boundary blocks beforehand with appropriate padding, the system avoids complex real-time processing during encoding, reducing overall processing complexity.
3Manufacturing precision
If recursive partitioning is performed on boundary blocks until no sub-block includes the boundary, then boundary processing accuracy is improved, but encoding time increases
Solution Approach 1:
Recursive partitioning is applied selectively only to boundary blocks rather than all blocks in the image. This partial application of recursion achieves accurate boundary processing while avoiding the time penalty of recursively partitioning the entire image, thus balancing precision and 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.