Automatic Contrast Enhancement via Histogram Peak Redistribution
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Solution Overview
Problem
Existing contrast enhancement techniques in digital image processing often result in unnatural images due to over-compensation, loss of details, and temporal inconsistency, particularly in video sequences with changing lighting conditions, as they fail to adaptively manage contrast across varying brightness levels and scenes.
Innovation Solution
A peak-preserving automatic contrast enhancement method that generates a histogram with discrete bins, identifies and redistributes pixel populations around peaks, computes an adaptive gain, and generates a transfer curve with a pivot point to maintain natural contrast and prevent over-stretching, while using recursive temporal filtering to ensure temporal stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If histogram equalization is used to improve contrast, then the contrast of the image is enhanced, but the brightness distribution becomes unnatural with dark images appearing too bright and bright images appearing too dark
Solution Approach 1:
The patent applies different contrast enhancement strategies to different regions of the histogram based on local pixel density characteristics. Instead of uniform histogram equalization, it identifies peak regions and applies selective contrast adjustment that preserves local brightness relationships, thereby maintaining natural appearance while enhancing contrast.
Solution Approach 2:
The patent employs dynamic adaptation by detecting peak positions and densities in real-time, and adjusting the contrast enhancement parameters accordingly. The system dynamically modifies the transfer function based on the actual histogram distribution, allowing it to adapt to different image types (dark, bright, low-contrast, high-contrast) and maintain natural brightness distribution.
2Illumination intensity
If manual contrast adjustment is used, then the dynamic range of video is stretched, but the adjustment cannot adapt to different source characteristics and fixed stretching characteristics are not suitable for all pictures
Solution Approach 1:
The system performs self-analysis by automatically detecting peak positions and densities in the histogram, and uses this self-detected information to drive the contrast enhancement process. The algorithm serves itself by using its own histogram analysis results to determine the appropriate enhancement strategy, eliminating the need for manual intervention while adapting to different source characteristics.
Solution Approach 2:
The patent dynamically changes enhancement parameters (contrast gain, pivot point position, transfer function shape) based on the detected histogram characteristics. By varying these parameters according to the actual image content, the system achieves adaptability to different source characteristics while maintaining natural appearance.
3Extent of automation
If existing automatic contrast enhancement methods are used, then contrast gain is adjusted automatically, but they have limitation on correcting over-contrasted pictures and may produce artifacts
Solution Approach 1:
The patent applies partial contrast enhancement by selectively targeting only the peak regions in the histogram rather than applying uniform enhancement across the entire range. This partial action approach prevents over-enhancement of already well-contrasted regions while still improving contrast in under-represented areas, thereby reducing artifacts and improving correction accuracy.
Solution Approach 2:
The system uses feedback from histogram peak detection to control the contrast enhancement process. By continuously monitoring the histogram distribution and adjusting the enhancement parameters based on detected peak characteristics, the system achieves more accurate contrast correction and avoids the limitations of fixed or overly aggressive enhancement methods.
4Illumination intensity
If mean brightness is mapped to itself in transform function, then the mean level is retained, but the perceived brightness may not be the same and lighting conditions may invert in consecutive video pictures
Solution Approach 1:
Instead of treating all brightness levels uniformly, the patent applies different transformation characteristics to different regions around the detected peak. The transfer function is locally optimized to preserve perceived brightness relationships rather than simply maintaining the mathematical mean, thereby improving perceived brightness consistency in video sequences.
Data Source
AI summary
In a process for enhancing contrast of an image having pixels in different brightness intensities, a histogram in discrete bins is generated. Each bin represents a pixel population of at least one pixel brightness intensity. A peak and a peak region of the histogram is then identified, wherein the peak region is a range of discrete bins around the peak. An average pixel population within the peak region is computed, and the pixel populations of the discrete bins within the peak region that exceeds the average pixel population are distributed. A transfer curve for mapping onto the image is then generated. The process can be used in an image processor for enhancing contrast of an image having pixel. Still further, a display having a receiver and a screen can include the foregoing image processor.