Face Image Color Correction via Probability-Based Brightness Adjustment
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Solution Overview
Problem
Existing image correction technologies fail to accurately apply color correction to digital images, particularly when dealing with monochrome images, leading to significant changes in image appearance and brightness discrepancies before and after correction.
Innovation Solution
An image correction apparatus and method that calculates a color correction value based on specific image data, adjusts this value based on probability calculations, and applies the correction to ensure the image's brightness and color remain consistent, using devices like color correction value calculating means, probability calculating means, and correcting means.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If color correction processing is applied to monochrome image data assuming it represents a color image, then the color of the face image can be corrected to desired skin tone, but the brightness of the image changes significantly and produces results widely different from the original
Solution Approach 1:
The system changes the parameter of color correction application based on image type detection. When monochrome image data is detected, the color correction value is adjusted (reduced or modified) to prevent excessive color correction that would alter brightness. This parameter adaptation allows the system to maintain brightness consistency while still providing appropriate color correction for color images.
Solution Approach 2:
The system incorporates feedback through image type detection that analyzes the characteristics of the input image data. Based on this feedback, the color correction value is dynamically adjusted. The detection result feeds back into the correction process to determine whether full color correction should be applied or modified, thereby preventing brightness changes in monochrome images.
2Manufacturing precision
If color conversion processing is applied to the entirety of image data to achieve predetermined target chromaticity value, then the color (skin tone) of the face area can be corrected, but the brightness of the face image before correction and after correction do not agree
Solution Approach 1:
The system applies different color correction characteristics to different image types. For monochrome images, the correction is locally adjusted to preserve brightness, while color images receive full correction. This local quality differentiation ensures that brightness consistency is maintained where needed without compromising color correction effectiveness.
Solution Approach 2:
The color correction value is changed based on the detected image type. When monochrome images are detected, the correction parameters are modified to maintain brightness while still achieving appropriate chromaticity correction for color images. This parameter change resolves the contradiction between correction precision and brightness consistency.
3Reliability
If probability-based color correction value adjustment is implemented, then extreme divergence in correction results can be prevented, but the device complexity increases with multiple calculating means
Solution Approach 1:
The system uses the image data itself to determine the appropriate correction level. The probability calculation analyzes the input image characteristics and automatically adjusts the correction value without requiring external intervention. This self-service mechanism improves reliability while keeping the complexity manageable by using the image's own properties as the control signal.
Solution Approach 2:
The correction value is dynamically changed based on probability calculations derived from image analysis. This parameter adjustment mechanism stabilizes correction results by adapting to the specific characteristics of each image, preventing extreme divergence while maintaining a relatively simple processing architecture through mathematical optimization.
Data Source
AI summary
On the basis of image data representing the area of a face image contained in an image represented by applied image data, a color correction value calculating circuit calculates a color correction value and a color image probability calculating circuit calculates the probability that the area of the face image is a color image. A color correction value adjusting circuit then adjusts the color correction value based upon the probability that the area of the face image is a color image. The adjusted color correction value is used in correction processing in an image correcting circuit.