Recursive Image Block Partitioning for Boundary-Aware 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
1Productivity
If image encoding is performed on a per block basis with fixed block sizes, then the encoding process is simplified and processing efficiency is improved, but the image size must be adjusted through padding to match block size multiples, causing loss of information and reduced compression efficiency
Solution Approach 1:
The image is divided into multiple blocks of different sizes rather than forcing a fixed block size. The partitioning unit recursively divides the image into blocks with sizes of 1/2, 1/4, 1/8, or 1/16 of the original image dimensions, allowing flexible segmentation that adapts to image content and boundary requirements without padding.
Solution Approach 2:
The block size is made dynamic rather than fixed. The encoding apparatus determines optimal block sizes adaptively based on image characteristics and boundary positions, allowing the system to adjust block dimensions to match image boundaries exactly, thereby eliminating the need for padding while maintaining efficient per-block processing.
2Ease of operation
If padding is applied to adjust image size to block size multiples, then per block encoding can be performed uniformly, but the compression efficiency deteriorates due to increased data volume and boundary artifacts
Solution Approach 1:
Instead of padding the entire image to a uniform block size, the image is segmented into multiple blocks of varying sizes (1/2, 1/4, 1/8, or 1/16 of original dimensions). This segmentation allows each block to be encoded uniformly while maintaining the original image dimensions and avoiding unnecessary data expansion.
Solution Approach 2:
Different blocks are assigned different sizes based on local image characteristics and boundary requirements. Regions requiring finer granularity receive smaller blocks, while other regions use larger blocks, optimizing compression efficiency locally without compromising uniform encoding processing across the entire image.
3Manufacturing precision
If recursive partitioning is performed to create sub-blocks including image boundaries, then boundary processing accuracy is improved and compression efficiency increases, but the device complexity and processing time increase
Solution Approach 1:
The partitioning unit employs a systematic recursive segmentation approach that divides blocks into sub-blocks with predetermined size ratios (1/2, 1/4, 1/8, or 1/16). This structured segmentation ensures that image boundaries are accurately captured within sub-blocks while maintaining a manageable hierarchical structure that does not excessively increase device complexity.
Solution Approach 2:
The system performs preliminary determination of optimal partition depths and block sizes before actual encoding. By pre-calculating the partitioning structure based on image characteristics and boundary positions, the system prepares an optimized hierarchical layout that achieves high boundary processing accuracy without requiring complex real-time decisions during encoding.
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.