GPU Image Segmentation for Region of Influence Determination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As image data sizes increase, existing software applications struggle to efficiently determine the regions of influence for transformed images, leading to slowed processing times and GPU capacity overload, as they often process the entire input image in a single stage.
Innovation Solution
Implementing systems and methods that segment the input image for execution by a GPU, using warp kernels and dummy data to dynamically determine a segmentation size, allowing the GPU to build the output image on a segment-by-segment basis, thereby reducing workload and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the entire input image is processed in a single stage, then the transformation is completed in one pass, but processing time increases and GPU capacity is exceeded
Solution Approach 1:
The patent divides the input image into multiple tiles or segments that can be processed independently and in parallel by the GPU. Each tile is processed separately through the transformation pipeline, allowing the GPU to handle smaller data chunks simultaneously, thereby increasing overall processing throughput without overwhelming the device capacity.
Solution Approach 2:
The patent performs preliminary calculations to determine the region of influence for each output pixel before actual image transformation. By pre-calculating which input pixels will contribute to each output pixel, the system prepares transformation data in advance, enabling more efficient GPU processing and reducing the computational burden during the actual transformation stage.
2Loss of time
If the entire input image is processed in a single stage, then no additional processing steps are needed, but processing time is slowed
Solution Approach 1:
The patent segments the image processing into distinct phases: region of influence determination, tile generation, and parallel GPU transformation. This segmentation allows each phase to be optimized independently, with the region of influence calculation performed once and reused across multiple tile processing operations, reducing redundant computations and overall processing time.
Solution Approach 2:
The patent calculates the region of influence for all output pixels in advance, which may exceed the actual boundaries of the final transformed image. This excessive calculation ensures that all necessary input pixels are identified for each output pixel, allowing for more flexible and efficient parallel processing of image tiles without missing any contributing pixels.
3Area of stationary object
If larger image sizes are processed, then more comprehensive image data is transformed, but the software application becomes unable to readily determine regions of influence
Solution Approach 1:
The patent divides large images into manageable tiles that can be processed independently. For each tile, the system determines the region of influence by calculating which input pixels contribute to the corresponding output tile pixels. This segmentation approach makes region of influence determination computationally feasible even for very large images by breaking down the problem into smaller, manageable subsets.
Solution Approach 2:
The patent performs preliminary region of influence calculations for all output pixels before generating actual image tiles. This pre-computation creates a lookup structure that maps output pixels to their contributing input pixels, making the subsequent tile generation and processing stages much more efficient even for large-scale images.
Data Source
AI summary
Embodiments are directed toward systems and methods segment an input image for performance of a warp kernel that executes by a graphics processing unit (GPU) the warp kernel on an array of dummy data, wherein cells of the array are populated with data representing the cells' respective locations within the array, determine, from an output array obtained from execution of the warp kernel on the dummy data, a segmentation size, and build by the GPU an output image from the input image by executing the warp kernel on the input image according to the segmentation size.