Gamma Correction Lookup Table Segmentation for Storage Optimization
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Solution Overview
Problem
Existing gamma correction systems face significant errors due to segment mapping and require large storage spaces, especially in system-on-chip (SoC) designs, as they convert high to low bit resolution signals.
Innovation Solution
A two-stage gamma correction method involving virtual index computation, primary and sub-index generation, gamma value construction, multi-threshold generation, and correction, which reduces storage space and errors by using a compressed lookup table scheme with reduced gate counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lookup table scheme is used to reduce significant error in gamma correction, then measurement precision is improved, but device complexity increases due to large storage space requirements
Solution Approach 1:
The patent divides the gamma correction lookup table into multiple segments, where each segment covers a specific range of input values. Instead of storing a complete high-resolution lookup table for all possible input values, the system stores multiple smaller segment tables that can be selectively accessed. This segmentation reduces the total storage space while maintaining correction precision through piecewise linear approximation.
Solution Approach 2:
The patent changes the representation parameters of the gamma correction data by using differential encoding and segment-based indexing. Instead of storing absolute correction values for all input levels, the system stores relative differences between adjacent segments and uses index parameters to locate the appropriate segment. This parameter transformation significantly reduces storage requirements while preserving correction accuracy.
2Device complexity
If a compressed lookup table scheme is used to reduce storage space, then device complexity is reduced, but measurement precision deteriorates due to approximation errors
Solution Approach 1:
By dividing the gamma correction function into multiple segments with different linear approximations, the system achieves better precision than a single compressed table. Each segment can be optimized for its specific input range, reducing the approximation error that would exist in a uniform compressed table. The segmentation allows the system to maintain high precision while using a compressed storage format.
Solution Approach 2:
The patent uses differential encoding where only the differences between adjacent lookup table entries are stored, rather than the complete values. This partial storage approach reduces storage space while allowing reconstruction of the full precision values through accumulation. The system stores minimal information (differences) that is sufficient to reconstruct the complete gamma correction function with high precision.
3Device complexity
If segment mapping with different slopes is used to approach gamma correction curve, then device complexity is reduced through linear approximation, but measurement precision deteriorates due to mapping errors
Solution Approach 1:
The patent divides the gamma correction curve into multiple segments, each with its own linear approximation parameters. By segmenting the curve, the system reduces the maximum deviation from the true gamma curve compared to a single linear approximation. Each segment can be independently optimized to minimize error within its range, achieving better overall precision while maintaining computational simplicity through linear operations.
Solution Approach 2:
The patent applies different linear approximation parameters (different slopes and intercepts) to different segments of the gamma correction curve. Instead of using a uniform linear approximation across the entire input range, each local segment has optimized parameters tailored to its specific characteristics. This local optimization minimizes approximation errors in each region while keeping the overall system computationally efficient.
Data Source
AI summary
A gamma correction method and system, which performs a gamma correction on an n-bit video signal to thus generate a gamma correction value. At first, a first primary index and a first sub-index are obtained by performing an index mapping on a value of the video signal. Next, a first gamma value is obtained by performing a table lookup on the first primary index and the first sub-index. Next, a second primary index and a second sub-index are obtained by performing the index mapping on the first gamma value. Next, multiple thresholds are obtained by performing the table lookup on the second primary index and the second sub-index. Finally, the value of the video signal and the multiple thresholds are compared to accordingly correct the first gamma value and generate the gamma correction value.