GPU Texture Resampling via 1D Algorithm Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern GPUs are incapable of efficiently performing complex texture resampling algorithms due to limitations in the number of instructions and sample points they can handle, leading to slow processing speeds when executing high-quality image transformations.
Innovation Solution
Decompose complex texture resampling algorithms into multiple one-dimensional transformations and communicate instructions to a GPU for separate execution, allowing the GPU to generate output images efficiently by performing each transformation in separate passes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex texture resampling algorithms (such as bicubic, b-spline bicubic, and Lanczos-sinc) are used to achieve high-quality image transformation, then manufacturing precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent segments complex 2D texture resampling algorithms into multiple independent 1D transformations. Each 1D transformation processes one dimension (horizontal or vertical) separately, reducing the computational complexity from O(n²) to O(n) while maintaining the same quality output. This segmentation allows the GPU to execute simpler, more efficient instructions for each dimension independently.
2Manufacturing precision
If complex texture resampling algorithms are implemented on modern GPUs, then manufacturing precision is improved, but productivity decreases due to limited instruction and sample point handling capacity
Solution Approach 1:
By dividing the resampling operation into separate 1D passes, each pass processes fewer sample points and requires fewer instructions per pixel. This segmentation enables the GPU to process each dimension efficiently within its instruction limits, then combine results to achieve the full 2D transformation quality without exceeding hardware constraints.
Solution Approach 2:
The patent transforms the problem from a 2D spatial transformation into multiple 1D transformations. This dimensional reduction allows the algorithm to achieve the same visual quality with significantly fewer computational operations per pass, enabling execution on GPUs with limited instruction and sample point capacity.
3Productivity
If simple resampling algorithms (such as nearest neighbor and bilinear interpolation) are used to improve processing speed on GPUs, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The patent implements complex high-quality resampling algorithms by segmenting them into multiple 1D passes that can be executed efficiently on GPUs. Each 1D pass uses simplified computations suitable for GPU architecture, but the composition of multiple passes achieves the quality of complex 2D algorithms, thus maintaining both speed and precision.
Data Source
AI summary
A system, a method and computer-readable media for performing texture resampling algorithms on a processing device. A texture resampling algorithm is selected. This algorithm is decomposed into multiple one-dimensional transformations. Instructions for performing each of the one-dimensional transformations are communicated to a processing device, such as a GPU. The processing device may generate an output image by separately executing the instructions associated with each of the one-dimensional transformations.