Self-Adaptive LUT Generation via First-Order Derivative Analysis
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Solution Overview
Problem
Current LUT generation methods for image signal processors are time-consuming and result in low accuracy due to manual curve observation and sampling point setting.
Innovation Solution
A self-adaptive LUT generation method that involves obtaining the target discrete function, processing it to obtain the first-order derivative, determining the coordinates of segment points based on the derivative and a preset number of segments, and generating the LUT from these coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual curve observation and manual sampling point setting are used for LUT generation, then the process allows flexibility in setting parameters, but the generation time is long and accuracy is low
Solution Approach 1:
The system performs self-adaptive LUT generation by automatically calculating optimal segment points and sampling points based on the target discrete function and its first-order derivative, eliminating the need for manual observation and setting while achieving high accuracy and efficiency
Solution Approach 2:
The method dynamically determines segment point coordinates by calculating the first-order derivative of the target discrete function and using it to identify inflection points and critical regions, automatically adjusting sampling density based on function characteristics rather than uniform manual spacing
2Productivity
If manual LUT generation methods are used, then parameter setting is flexible, but the process complexity and time consumption increase
Solution Approach 1:
The patent replaces manual mechanical observation and setting processes with automated computational methods, using mathematical operations (first-order derivative calculation, inflection point detection) to automatically determine optimal LUT parameters, thereby improving efficiency while managing complexity through algorithmic approaches
3Measurement precision
If uniform sampling is used for LUT generation, then the process is simple, but accuracy is reduced in regions with high curvature changes
Solution Approach 1:
The method applies non-uniform sampling density based on local function characteristics by using the first-order derivative to identify regions with high curvature changes (inflection points), concentrating sampling points in these critical regions while using fewer points in stable regions, thereby achieving high accuracy without excessive complexity
Data Source
AI summary
The present disclosure provides a method, apparatus, electronic device, and storage medium for self-adaptive LUT generation. The method includes: obtaining the target discrete function configured in a specific functional unit for task processing, processing the target discrete function to obtain the first-order derivative of the target discrete function, determining the coordinates of the segment points of the target discrete function based on the first-order derivative and the preset number of segments, and generating an LUT based on the coordinates of the segment points of the target discrete function. With this method, LUTs can be automatically generated based on the target discrete function with high efficiency. Determining the segment points of the target discrete function using the aforementioned method also improves the accuracy of the LUTs.