VR Image Distortion Correction via Regional Grid Density
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Solution Overview
Problem
Virtual reality (VR) display devices face challenges in processing large amounts of image data due to limited rendering capabilities, leading to reduced anti-distortion effects and frame loss, especially when using grid anti-distortion processing methods.
Innovation Solution
An image processing method that partitions an original gridded image into regional grid images distributed away from the geometric center, adjusting grid vertices based on anti-distortion parameters to form regional correction grid images with gradually increasing grid densities, reducing the number of grid vertices and data processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If grid anti-distortion processing method is used on original image data, then image distortion is reduced, but the amount of data processing increases
Solution Approach 1:
The original gridded image is divided into multiple regional grid images based on distortion coefficient distribution characteristics. Each regional image is processed independently with appropriate grid density, reducing the overall computational burden while maintaining anti-distortion effectiveness in critical regions.
Solution Approach 2:
Different grid densities are applied to different regions of the image based on their distortion characteristics. Regions with higher distortion coefficients receive higher grid density for better anti-distortion effect, while regions with lower distortion use lower grid density to reduce processing load.
2Manufacturing precision
If high grid density is used throughout the original image, then anti-distortion effect is improved, but processing time increases and frame loss occurs
Solution Approach 1:
The image is segmented into regions with different grid density requirements. By processing only necessary regions with high grid density and using lower grid density in other regions, the overall processing time is reduced while maintaining anti-distortion effect where needed.
Solution Approach 2:
Instead of applying uniform high grid density across the entire image, the method applies partial action by concentrating grid processing only in regions where distortion coefficients indicate it is necessary, thereby reducing unnecessary processing in low-distortion areas.
3Device complexity
If VR display device has limited rendering capability, then hardware requirements are reduced, but frame loss increases and image quality deteriorates
Solution Approach 1:
By dividing the image into regions and processing them with appropriate grid densities, the rendering workload is distributed and reduced, making it feasible for devices with limited rendering capabilities to process the data without excessive frame loss.
Solution Approach 2:
The method dynamically adjusts grid density parameters based on distortion coefficient distribution and device capabilities. This allows the system to optimize the balance between processing load and output quality, reducing frame loss on devices with limited rendering capability.
Data Source
AI summary
An image processing method is disclosed. The image processing method may include: partitioning an original gridded image to obtain a plurality of regional grid images, wherein the regional grid images are distributed along a direction away from a geometric center of the original gridded image (S300); and adjusting grid vertices of the regional grid images based on anti-distortion parameters to obtain a plurality of regional correction grid images forming a grid correction image (S400). A grid density of each of the regional correction grid images may be smaller than or equal to a grid density of a corresponding regional grid image, and the grid density of each of the plurality of the regional correction grid images may gradually increase along the direction away from the geometric center of the original gridded image.