Data Field Transformation via Segmented Lookup Tables
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Solution Overview
Problem
Existing methods for transforming data fields in hardware applications, such as image post-processing, are resource-intensive due to the need for large lookup tables and interpolation of missing points, which consume significant memory and processing resources.
Innovation Solution
Divide the data field into segments defined by functions, such as polynomials, allowing for location-dependent transformation rules without the need for closed formulas or extensive lookup tables, enabling efficient storage and calculation by selecting and applying the appropriate transformation rule based on the segment of each data point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lookup tables are used for each coordinate point to store transformation values, then transformation precision is improved, but memory resource consumption increases significantly
Solution Approach 1:
The data field is divided into multiple segments based on spatial coordinates. Instead of creating lookup tables for all coordinate points, separate lookup tables are created only for boundary points of each segment. This segmentation approach reduces the total number of lookup table entries from O(N*M) to O(k*(N/M + M/N)) where k is the number of segments, significantly reducing memory consumption while maintaining transformation precision through localized accuracy.
2Quantity of substance
If smaller lookup tables are used with interpolation for missing points, then memory resource consumption is reduced, but calculation complexity increases
Solution Approach 1:
The patent applies different processing strategies to different regions: boundary points use direct lookup table retrieval while interior points use linear interpolation. This local quality approach optimizes the balance between memory usage and calculation complexity by applying interpolation only where necessary (interior points) rather than universally, reducing overall calculation complexity compared to full interpolation approaches.
3Measurement precision
If location-dependent transformation rules are implemented without segmentation, then transformation accuracy is improved, but device complexity increases due to needing individual rules for each data point
Solution Approach 1:
The patent segments the data field into regions and assigns transformation rules at the segment level rather than individual data point level. This allows location-dependent transformation accuracy to be maintained while reducing device complexity, as the system only needs to manage transformation rules for segment boundaries rather than for every coordinate point.
Solution Approach 2:
The patent creates a universal framework where a small number of lookup tables at segment boundaries serve multiple purposes: they define segment boundaries, provide transformation values for boundary points, and serve as reference points for interpolating interior points. This multi-functionality reduces device complexity by eliminating the need for separate individual transformation rules for each data point.
Data Source
AI summary
A method and a device for applying a transformation to a data field are provided, wherein data points are transformed using at least one transformation rule, characterized in that the data field is divided into segments, that for each data point to be transformed of the data field a test is performed to identify the segment in which the data point is arranged, and that a transformation rule is subsequently selected depending on the segment found and the rule is applied to said data point.