Image Encoding Boundary Partitioning for Arbitrary Image Sizes
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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 a multiple of the block size which causes difficulty in processing images with arbitrary dimensions
Solution Approach 1:
The image is divided into multiple blocks of predetermined sizes (e.g., 64x64, 32x32, 16x16 pixels) through hierarchical segmentation. This allows the encoding process to work with standardized block sizes while still accommodating images of arbitrary dimensions by creating a tree structure of blocks that covers the entire image area.
Solution Approach 2:
The patent introduces a hierarchical block structure with multiple levels (first blocks, second blocks, third blocks) where each level divides the previous level's blocks. This multi-dimensional approach allows flexible adaptation to different image sizes while maintaining efficient per-block processing at each hierarchical level.
2Productivity
If the image size is adjusted to be a multiple of block size through padding, then per block encoding can be performed efficiently, but additional processing steps and memory usage are required
Solution Approach 1:
Padding is performed in advance during the block segmentation phase, before the actual encoding process. By pre-adjusting the image dimensions and creating the block hierarchy structure beforehand, the main encoding process can proceed efficiently without needing to handle size adjustments during processing.
Solution Approach 2:
The padding operation is extracted as a separate preliminary step from the main encoding process. This allows the encoding algorithm to focus on processing complete blocks without being burdened by size adjustment operations, improving overall processing efficiency.
3Loss of energy
If neighboring block information is utilized for prediction, then compression efficiency is improved, but the complexity of determining which neighboring blocks to use increases
Solution Approach 1:
The patent applies different prediction strategies to different blocks based on their local characteristics and positions. Each block independently determines which neighboring blocks to use for prediction, allowing the system to adapt to local image features while maintaining a systematic approach to neighboring block selection.
Solution Approach 2:
Each block autonomously determines its own prediction parameters by selecting appropriate neighboring blocks based on its position and characteristics. This self-service approach reduces the need for complex centralized control in determining neighboring block usage, simplifying the overall system complexity.
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.