Dynamic Gamma Correction Using Histogram-Based Curve Adaptation
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Solution Overview
Problem
Conventional dynamic gamma correction techniques suffer from image artifacts, high computational complexity, and flicker issues due to the need for complex algorithms and limited memory in handling non-linear gamma curves, especially in fast or slow-moving image sequences.
Innovation Solution
A method for dynamically computing non-linear gamma curves using histogram data from each video frame, applying gray scale correction to each pixel based on computed gamma values, and adapting curve changes to minimize flicker by using a predetermined number of bins and threshold values for adaptive gamma correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If piecewise linear approximation is used for dynamic gamma correction, then the transfer characteristic curve can closely approximate non-linear characteristics, but visible image artifacts occur and complex algorithms with more logic are required
Solution Approach 1:
The patent changes the fundamental parameter representation from piecewise linear segments to a continuous non-linear function defined by key point coordinates. Instead of using multiple linear segments with varying slopes, the system represents the gamma curve as a series of (x, y) coordinate pairs that define a smooth non-linear transfer characteristic, eliminating the need for complex segment switching logic while maintaining approximation accuracy.
2Productivity
If look-up table technique is used to store pre-computed characteristics curves, then gamma correction can be applied efficiently, but the number of curves is limited by memory size
Solution Approach 1:
The patent transforms the fixed LUT approach into a dynamic parameter-based system. Instead of storing complete curve data in memory, the system stores minimal parameters (key point coordinates) that define the gamma curve characteristics. These parameters are computed on-demand based on current image histogram analysis, allowing the system to adapt to different image conditions without being constrained by pre-stored curve limitations.
Solution Approach 2:
The system performs self-service by computing the required gamma correction parameters dynamically from the input image characteristics itself. The histogram analysis of the current frame automatically determines the appropriate gamma curve parameters, eliminating the need for external storage of multiple pre-computed curves and enabling the system to generate appropriate correction parameters for any image condition.
3Stability of the object's composition
If histogram temporal filtering technique is used to overcome flicker, then curve changes can be smoothed, but highly intensive computation of filter coefficients is required
Solution Approach 1:
The patent applies partial action by using a limited number of key points (e.g., 5-10 points) to define the gamma curve instead of analyzing the entire histogram or using complex temporal filtering across all pixel values. This selective approach provides sufficient flicker reduction through smooth parameter transitions while avoiding the intensive computation of comprehensive temporal filters.
Data Source
AI summary
A technique for improving image quality using dynamic gray scale correction, in one example embodiment, includes dynamically computing non-linear gamma curves using histogram data extracted from a current video frame. A gray scale correction is then dynamically applied to each pixel in the current or next video frame as a function of the computed non-linear gamma curves.