Display Image Adjustment for Environmental Adaptation
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Solution Overview
Problem
Conventional display technologies struggle to maintain image quality when adjusting backlight intensity in response to environmental changes, often causing pattern distortion in bright or dark zones, which can be harsh on the user's eyes.
Innovation Solution
An image adjusting method that generates a gray level histogram, calculates pixel amounts in boundary zones, and uses amending functions to adjust pixel intensity based on threshold comparisons, allowing for self-adaptive adjustments in backlight and color parameters to prevent pattern distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the backlight intensity is adjusted according to surrounding illumination, then the display adapts to environmental variation, but the image quality deteriorates due to pattern distortion in bright or dark zones
Solution Approach 1:
The patent divides the image into multiple zones based on gray level histogram analysis, specifically identifying boundary zones (bright and dark regions) separately from middle zones. This segmentation allows different adjustment strategies to be applied to different zones, preventing pattern distortion in boundary zones while maintaining overall adaptability to environmental illumination changes.
Solution Approach 2:
The patent applies local quality adjustment by using zone-specific amending functions for boundary zones while applying different or no adjustments to middle zones. The adjustment parameters and functions are tailored to the characteristics of each zone, ensuring that bright and dark regions are handled differently to prevent distortion while adapting to environmental conditions.
2Manufacturing precision
If the pixel intensity of boundary zones is adjusted using amending functions, then the pattern distortion is reduced, but the device complexity increases
Solution Approach 1:
The patent performs preliminary analysis by generating a gray level histogram and calculating pixel amounts in boundary zones before applying adjustments. This preliminary action identifies which zones require adjustment and selects appropriate amending functions in advance, streamlining the subsequent adjustment process and reducing overall system complexity despite the sophisticated adjustment mechanism.
Solution Approach 2:
The patent applies adjustment only to boundary zones (bright and dark regions) identified through histogram analysis, rather than adjusting all pixels uniformly. This partial action approach focuses computational resources on the specific zones that require correction, reducing overall processing complexity while effectively preventing pattern distortion in critical areas.
3Illumination intensity
If the backlight intensity is increased in bright environments, then the image visibility is improved, but the user comfort deteriorates due to harsh illumination
Solution Approach 1:
The patent dynamically changes illumination parameters based on environmental conditions and image content characteristics. By analyzing the gray level histogram and identifying boundary zones, the system adjusts pixel intensity parameters selectively in bright and dark regions, ensuring optimal visibility while maintaining user comfort across different environmental illuminations.
Data Source
AI summary
An image adjusting method capable of executing optimal adjustment according to environmental variation is applied to a related display. The image adjusting method includes generating a gray level histogram of an image, calculating a pixel amount of a boundary zone on the gray level histogram, comparing the pixel amount with a threshold, and utilizing an amending function to adjust the pixel intensity of the boundary zone while the pixel amount is greater than the threshold.