Uneven Sample Interpolation via Precomputed Lookup Tables
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Solution Overview
Problem
Existing image processing methods face inefficiencies when using unevenly spaced samples for interpolation, requiring time-consuming search operations to determine the appropriate sample points for accurate image data conversion.
Innovation Solution
A method is developed to efficiently identify and utilize unevenly spaced sample points for interpolation by generating a data structure with evenly spaced index values, allowing for quick lookup and interpolation of image data using Graphics Processing Unit (GPU) textures, enabling rapid conversion and processing of image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search operation (e.g., binary search) is used to determine where the input value falls in the set of x-value sample points, then the interpolation accuracy is maintained, but the processing time increases
Solution Approach 1:
The patent pre-calculates and stores index values in a lookup table during an initialization phase. Each entry in the lookup table contains pre-computed indices that directly map input ranges to the corresponding unevenly spaced sample points. During actual interpolation operations, the system simply performs a table lookup instead of executing search algorithms, thereby maintaining accuracy while dramatically reducing processing time.
2Productivity
If evenly spaced samples are used for interpolation, then the indexing operation is simple and fast, but the resolution cannot be adjusted in particular sub-ranges
Solution Approach 1:
The patent implements unevenly spaced sampling where different regions of the input domain have different sample densities. By concentrating more sample points in sub-ranges requiring higher resolution and using fewer points in less critical regions, the system achieves both high indexing efficiency through pre-computed lookup tables and adaptive resolution control tailored to specific application requirements.
Data Source
AI summary
The present disclosure includes systems and techniques relating to identifying function samples for use in interpolation. In general, in one implementation, the technique includes: obtaining a set of unevenly spaced sample points, x, and corresponding sample outputs, y, of a function y=f(x) within a domain; generating a data structure including first locations corresponding to a set of evenly spaced sample points within the domain, wherein the first locations store index values corresponding to second locations storing the sample outputs, y; receiving an input value of image data; identifying one of the first locations corresponding to the input value; finding two index values stored in the first locations using the one of the first locations as a starting point; and generating an image data output value corresponding to the input value by interpolation using two sample outputs, y, obtained from the second locations using the two index values.