Image Displacement Point Array Generation for Storage Reduction
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Solution Overview
Problem
Current image processing and storage methods require significant resources due to the large amount of data needed to represent entire images, especially for 3D images, which can be inefficient for storage and processing.
Innovation Solution
Generating a 2D array of points that identifies displaced or edge portions of an image, allowing for a reduced number of points to represent the image, thereby minimizing storage space and processing resources, using a processor to determine displacement levels, select sampling density, and generate an array of points that samples these displaced portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire image is processed and stored, then complete image information is preserved, but data size and storage resources increase significantly
Solution Approach 1:
The patent extracts only the essential edge and displacement information from the complete image, discarding redundant data. By identifying and retaining only the boundary points and displacement vectors that define the image structure, the system achieves significant data reduction while preserving the essential visual information needed for reconstruction.
Solution Approach 2:
Instead of storing the complete original image data, the patent creates a simplified representation copy consisting of edge points and displacement vectors. This copy contains the essential structural information of the image in a compressed form, enabling reconstruction with minimal data storage requirements.
2Measurement precision
If more points are used to represent the image, then image precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the image processing task into identifying only the critical edge portions and displacement regions. By dividing the image into essential structural components (edges and displacements) rather than processing every pixel uniformly, the system achieves efficient processing with reduced computational overhead while maintaining adequate precision for the application.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the image (edges and displacement areas) rather than the entire image. This selective processing approach uses fewer computational resources and reduces processing time while still capturing the essential features needed for the intended application.
3Loss of information
If full image data is stored, then complete visual information is available, but storage space requirements increase
Solution Approach 1:
The patent extracts only the essential edge and displacement information from the complete image, discarding redundant data. By identifying and retaining only the boundary points and displacement vectors that define the image structure, the system achieves significant data reduction while preserving the essential visual information needed for reconstruction.
4Measurement precision
If sampling density is increased, then displaced portions are more accurately identified, but the number of points and data size increase
Solution Approach 1:
The patent applies different sampling densities to different regions of the image based on their importance. Critical edge portions and areas with significant displacement are sampled with higher density, while uniform or less important areas use lower sampling density. This localized quality adjustment ensures accurate displacement detection where needed while minimizing the total number of points required.
Data Source
AI summary
According to examples, an apparatus may include a processor and a non-transitory computer readable medium on which is stored instructions that the processor may execute to determine displacement levels of multiple locations of an image and to select a sampling density for the multiple locations. The processor may execute the instructions to generate an array of points according to the selected sampling density, the array of points identifying displaced portions of the image.