Gamma Correction Using 1st-Order Power Functions
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Solution Overview
Problem
Existing gamma correction methods require significant memory space for multiple gamma coefficients and involve large multipliers, making them unsuitable for compact design, especially when transforming multiple gamma coefficients.
Innovation Solution
The gamma curve is divided into sections approximated by 1st-order power functions, replacing multipliers with shifting operations to simplify the architecture and reduce hardware costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If look-up tables (LUTs) are used to implement gamma correction, then transformation accuracy is improved, but memory space occupation increases significantly
Solution Approach 1:
The gamma curve transformation is divided into multiple sections, with each section approximated by a 1st-order power function. This segmentation allows the use of simpler linear transformations instead of storing complete LUT data, significantly reducing memory requirements while maintaining acceptable accuracy within each segment.
Solution Approach 2:
The patent replaces expensive memory storage (LUTs) with inexpensive computational operations (shifters and adders). Instead of storing pre-computed transformation values in memory, the system computes transformations on-the-fly using simple arithmetic operations, effectively trading memory space for computational simplicity.
2Measurement precision
If 2nd-order power functions are used to approximate gamma curve sections, then transformation accuracy is improved, but hardware complexity increases due to required multipliers
Solution Approach 1:
The patent changes the mathematical order of the approximation function from 2nd-order to 1st-order power functions. This parameter change eliminates the need for multiplication operations, allowing implementation using only shifters and adders, thereby significantly reducing hardware complexity while maintaining functional adequacy for gamma correction.
3Adaptability or versatility
If multiple gamma coefficients are supported, then adaptability is improved, but memory space increases monotonically
Solution Approach 1:
The patent creates a universal transformation architecture using 1st-order power functions that can handle multiple gamma coefficients through the same computational pathway. Instead of requiring separate LUTs for each gamma coefficient, the system uses a single set of shifters and adders that can be configured to implement different gamma transformations, making the hardware multi-functional and eliminating monotonically increasing memory requirements.
Data Source
AI summary
This invention discloses a transforming apparatus, according to a transforming curve, for transforming a first image to a second image. The first image includes a plurality of pixels. The transforming apparatus includes a differentiating module, a first transforming module, an adjusting module and a second transforming module. According a predetermined value, the differentiating module transforms the transforming curve into a plurality of sub-curves. The first transforming module generates a plurality of straight lines corresponding to the sub-curves. According to the sub-curves, the straight lines and a predetermined threshold, the adjusting module adjusts the straight lines to generate a plurality of approximate lines. According to the approximate lines and the pixels of the first image, the second transforming module generates the second image.