Image Block Partitioning for Boundary-Aware Video Encoding
Find Innovative SolutionsGenerate Solutions
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 it becomes difficult to efficiently process images with sizes that are not multiples of block size and to effectively utilize neighboring block information at boundaries
Solution Approach 1:
The image is divided into multiple blocks of predetermined sizes (e.g., 64x64, 32x32, 16x16 pixels) through systematic segmentation. This allows the encoding process to handle images of various sizes by partitioning them into manageable blocks, while still maintaining encoding simplicity through standardized block processing procedures.
Solution Approach 2:
Different encoding strategies are applied to different regions of the image based on their characteristics. Specifically, boundary blocks receive special treatment with enhanced utilization of neighboring block information, while internal blocks use standard encoding processes. This local differentiation improves boundary processing capability without compromising overall encoding efficiency.
2Stability of the object's composition
If standard block partitioning is used without considering boundary conditions, then processing uniformity is maintained, but compression efficiency deteriorates due to ineffective utilization of neighboring block information
Solution Approach 1:
The patent applies different processing qualities to different block locations. Boundary blocks are identified and processed with special attention to neighboring block information, while internal blocks use uniform processing. This localized quality enhancement improves compression efficiency at critical boundary regions without disrupting the overall uniform processing framework.
Solution Approach 2:
The encoding process incorporates feedback mechanisms where neighboring block information is utilized to improve the encoding of current blocks. This feedback loop allows the system to adaptively use information from already-encoded neighboring blocks to enhance compression efficiency, particularly at image boundaries where context information is most valuable.
3Manufacturing precision
If recursive partitioning is performed on blocks containing image boundaries, then boundary processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent systematically segments blocks containing image boundaries into smaller sub-blocks through recursive partitioning. This segmentation continues until sub-blocks are small enough to be effectively processed with boundary-specific techniques. The structured segmentation approach improves boundary processing accuracy by creating appropriately-sized units for detailed analysis.
Solution Approach 2:
The partitioning process is made dynamic and adaptive based on block characteristics. Blocks containing image boundaries are automatically identified and subjected to recursive partitioning, while blocks without boundaries use standard partitioning. This dynamic approach concentrates computational resources where they are most needed (at boundaries) without unnecessarily increasing complexity throughout the entire image processing system.
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.