Automated Low Contrast Detection in Images
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Solution Overview
Problem
Conventional methods for detecting contrast in images are subjective and prone to human error, leading to inconsistent results and difficulties in identifying low contrast regions, especially when displayed on different media.
Innovation Solution
An automated process using a computer system to detect edges and evaluate contrast values in component images, generating a delicate area map and applying morphological functions to enhance visibility of small or thin objects, and adjusting transparency and typography to increase contrast in low contrast regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for contrast detection, then the process is simple and requires minimal equipment, but the results are inconsistent and prone to human error
Solution Approach 1:
The patent replaces the mechanical/human-based contrast detection system with an automated computer vision system. The system uses image processing algorithms to objectively measure contrast ratios between different regions of an image, eliminating human subjectivity and inconsistency while maintaining computational simplicity through standard image processing techniques.
Solution Approach 2:
The system performs self-measurement of contrast properties through automated image analysis. The computer system independently evaluates contrast ratios without requiring human reviewers, using algorithmic processing to consistently quantify contrast differences across images and display media.
2Measurement precision
If human reviewers are used to identify low contrast regions, then the method is straightforward, but small low contrast regions are often missed or not seen
Solution Approach 1:
The patent substitutes human visual inspection with automated image processing that can detect and measure contrast ratios at any resolution. The system processes images pixel-by-pixel or region-by-region, capable of identifying small low contrast regions that would be imperceptible to human reviewers while maintaining rapid processing speeds.
Solution Approach 2:
The system transitions from two-dimensional visual perception to multi-dimensional quantitative analysis. Instead of relying on human eyes to perceive contrast, the system measures contrast ratios as numerical values across multiple regions, enabling detection of small regions through computational analysis rather than visual inspection.
3Adaptability or versatility
If conventional contrast detection is used, then the process is quick, but results vary depending on the media on which the image is displayed
Solution Approach 1:
The patent employs parameter-based adaptation by measuring actual contrast ratios in the displayed image rather than relying on fixed theoretical values. The system captures real-world contrast data from the display medium itself, allowing accurate measurements across different media (projectors, monitors, mobile devices) by directly analyzing the rendered image properties.
Solution Approach 2:
The system incorporates feedback from actual display performance by analyzing the rendered image as it appears on the specific display medium. This feedback loop allows the system to adapt to media-specific characteristics and provide accurate contrast measurements that reflect actual viewing conditions rather than idealized parameters.
Data Source
AI summary
A method includes generating a delicate area map by performing a morphological function on a portion of a received first image and identifying a plurality of edges in the first image, the plurality of edges comprising a plurality of pixels. The method also includes verifying a first contrast metric for a first subset of pixels that are in the plurality of pixels but not in the delicate area map, verifying a second contrast metric for a second subset of pixels that are in the plurality of pixels and in the delicate area map, and generating a validation result based on the verifying of the first contrast metric and the verifying of the second contrast metric.