Adaptive Contrast Enhancement Using Weighted Primitive Histograms
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Solution Overview
Problem
Traditional contrast adjustment methods in displays result in unintended average brightness shifts and saturation or clipping, especially when increasing or decreasing contrast in images with varying luminance levels, leading to poor enhancement outcomes.
Innovation Solution
An adaptive contrast enhancement method that expands the number of available reference transfer curves without increasing luminance ranges by generating a luminance histogram as a weighted sum of predefined primitive histograms, with each primitive histogram associated with a reference transfer curve, to produce a final transfer curve for adjusting image luminance and enhancing contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional contrast adjustment methods are used, then contrast can be increased or decreased, but unintended average brightness shifts and saturation or clipping occur
Solution Approach 1:
The patent applies parameter changes by using a luminance histogram to characterize the input image and expressing it as a weighted sum of primitive histograms. Each primitive histogram has an associated reference transfer curve, and the final transfer curve is produced as a corresponding weighted sum. This allows the contrast enhancement to adapt to different image luminance distributions, preventing unintended brightness shifts and saturation while maintaining accurate brightness control.
2Measurement precision
If the number of luminance ranges is increased to expand reference transfer curves, then more precise contrast enhancement is achieved, but hardware complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the luminance histogram into a set of predefined primitive histograms, each representing a specific luminance distribution pattern. Instead of increasing the number of luminance ranges, the method segments the histogram space and uses a weighted sum of a limited number of reference transfer curves corresponding to these primitives. This achieves precise contrast enhancement while keeping hardware complexity manageable.
3Illumination intensity
If contrast is increased on an image with good contrast, then brightness enhancement is achieved, but saturation or clipping occurs
Solution Approach 1:
The patent applies feedback by using the luminance histogram of the input image to determine the appropriate transfer curve. The histogram is expressed as a weighted sum of primitive histograms, and this information feeds into selecting and combining the corresponding reference transfer curves. This feedback mechanism ensures that contrast enhancement is applied appropriately based on the actual image content, preventing saturation and clipping by avoiding over-enhancement of already high-contrast images.
Data Source
AI summary
Disclosed is a method for generating transfer curves for adaptive contrast enhancement. Given an input image, a luminance histogram is generated based on a set of predefined input luminance ranges. The luminance histogram is then expressed as a weighted sum of a set of predefined “primitive” histograms. Each primitive histogram has an associated reference transfer curve. A final transfer curve is produced as a corresponding weighted sum of the associated reference transfer curves. The image luminance can then be adjusted according the to the final transfer curve, resulting in enhanced image contrast. The disclosed method enables expansion of the number of available reference transfer curves without increasing the number of luminance ranges.