Video Contrast Enhancement via Histogram-Based Mapping Function Selection
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Solution Overview
Problem
Existing methods for enhancing video contrast often result in over-correction, complexity, or lack of accuracy in selecting appropriate mapping functions, leading to suboptimal picture quality with potential artifacts like flickering.
Innovation Solution
A method that determines the characteristics of a video signal's histogram, selects a suitable mapping function from a set of predefined functions based on these characteristics, and applies it to enhance contrast without introducing artifacts, using techniques like temporal filtering and adaptive weighting to prevent flickering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If mapping functions are generated by the CDF of the picture histogram, then contrast enhancement is achieved, but over-correction and over-contrast pictures result
Solution Approach 1:
The patent modifies the parameters used to generate mapping functions by incorporating multiple histogram statistics (mean, standard deviation, skewness, kurtosis) rather than relying solely on CDF. This parameter expansion allows for more nuanced control over contrast enhancement, preventing over-correction while maintaining improvement in picture contrast.
Solution Approach 2:
The system dynamically selects different mapping function types (linear, power, logarithmic, reciprocal) based on real-time analysis of histogram characteristics. This dynamic adaptation allows the contrast enhancement to respond appropriately to different image content, avoiding over-contrast in certain regions while enhancing others.
2Measurement precision
If picture pre-processing such as quantization or filtering is performed to generate mapping functions, then mapping function accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary computation of histogram statistics (mean, standard deviation, skewness, kurtosis) on the input image data before generating mapping functions. This preliminary analysis captures essential characteristics of the image distribution, enabling accurate mapping function selection without requiring complex post-processing or iterative optimization.
Solution Approach 2:
The patent replaces complex mechanical or iterative processing methods with direct mathematical computation of histogram statistics. By using closed-form calculations of mean, standard deviation, skewness, and kurtosis, the system achieves high mapping function accuracy without the computational burden of iterative filtering or quantization processes.
3Ease of operation
If only average brightness level is used to select mapping curve types, then processing simplicity is maintained, but selection accuracy is insufficient
Solution Approach 1:
The patent expands the set of parameters used for mapping function selection from a single average brightness metric to four histogram statistics (mean, standard deviation, skewness, kurtosis). This parameter expansion significantly improves selection accuracy while maintaining computational efficiency through direct mathematical formulas.
Solution Approach 2:
The patent segments the histogram analysis into distinct statistical components (central tendency, dispersion, asymmetry, tail characteristics) that can be independently calculated and combined. This segmentation allows for a comprehensive yet computationally efficient characterization of image luminance distribution for accurate mapping function selection.
4Adaptability or versatility
If a system has selectable mapping functions but lacks automatic selection mechanism, then mapping function versatility is provided, but system complexity and design changes increase
Solution Approach 1:
The patent implements a self-service automatic selection mechanism that uses histogram statistics to autonomously determine the most appropriate mapping function type. The system evaluates multiple mapping function candidates based on computed statistics and automatically selects the optimal one without requiring external intervention or complex control logic.
Solution Approach 2:
The patent uses changes in histogram parameters (mean, standard deviation, skewness, kurtosis) as the basis for automatic mapping function selection. By monitoring these statistical parameters, the system can automatically adapt to different image characteristics and select appropriate mapping functions, providing versatility without increasing overall system complexity.
Data Source
AI summary
A method for enhancing the contrast of video pictures that includes the steps of receiving an input video signal; extracting a picture from said input video signal; determining an active window for said picture; calculating a histogram for luminance values of pixels in said active window of said picture; determining characteristics of said histogram; selecting one suitable mapping function from a plurality of mapping functions based on the determined characteristics of said histogram; and mapping the luminance value of each pixel in said picture in accordance with said selected mapping function.