Image Saturation Adjustment via Local Deviation Detection
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Solution Overview
Problem
Conventional methods for adjusting image contrast and saturation in electronic devices are inefficient, as they rely on statistical data calculations and table lookups, which do not effectively enhance image quality for users with varying preferences.
Innovation Solution
A method that calculates a deviation level between a pixel and its vicinity to adjust saturation and contrast, using a global gain and local contrast to modify brightness and saturation values, allowing for more precise image adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional statistical data calculation and table lookup methods are used to adjust contrast and saturation, then the adjustment process is simple to implement, but the image quality enhancement is insufficient and cannot meet varying user preferences
Solution Approach 1:
The patent applies local quality by calculating local contrast for different regions of the image separately. Instead of applying a uniform adjustment to the entire image, the method computes luminance averages for specific pixel sets in different areas, allowing each region to be adjusted according to its local characteristics. This enables precise control over image quality in different zones while maintaining a manageable process complexity through systematic regional division.
Solution Approach 2:
The patent segments the image processing into distinct computational stages: calculating luminance for individual pixels, computing local contrast for pixel sets, determining global gain for the entire area, and finally applying modified brightness values. This segmentation of the adjustment process into modular steps makes the complex image quality enhancement manageable and implementable while achieving superior results compared to conventional table lookup methods.
2Adaptability or versatility
If uniform brightness adjustment is applied to the entire image area, then the adjustment process is simple, but it cannot account for local brightness variations and reduces image naturalness
Solution Approach 1:
The patent implements local quality by computing separate luminance averages for different pixel sets within the image area. Each pixel's brightness adjustment is based on its local neighborhood characteristics rather than a global average, allowing the system to adapt to local brightness variations while maintaining processing efficiency through a systematic approach to regional analysis.
Solution Approach 2:
The patent applies dynamics by making the brightness adjustment adaptive rather than static. The local contrast calculation and global gain determination create a dynamic adjustment mechanism that responds to the actual luminance characteristics of different image regions. This dynamic approach allows the system to efficiently adapt to varying brightness conditions across the image while maintaining overall processing productivity.
Data Source
AI summary
A method for adjusting saturation of an area of an image is disclosed. The method includes calculating a deviation level of a pixel in the area of the image by a processor and calculating a modified saturation value of the pixel in the area of the image according to the deviation level of the pixel in the area of the image and an original saturation value of the pixel in the area of the image by the processor. The deviation level indicates brightness differences of the brightness values among the pixel and a pixel set in a vicinity of the pixel in the area of the image. The pixel set includes pixels in the vicinity of the pixel of the area of the image.