Local Value Adjustment for Image Contrast and Color Accuracy
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Solution Overview
Problem
Display devices face limitations in producing life-like images due to their inherent contrast ratio, which is insufficient to accurately map real-world brightness variations, leading to issues like noise and unnatural image appearance when using existing contrast enhancement methods.
Innovation Solution
A system and method for local value adjustment that applies a locally-optimized transfer function to image pixels based on hue, saturation, and brightness, selectively enhancing contrast and saturation while avoiding unwanted effects on skin tones and dark colors, using a two-dimensional look-up table and sigmoid-like transfer functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If a global transfer function is used to enhance contrast, then the contrast of the input image is improved, but noise and compression artifacts are increased
Solution Approach 1:
The image is divided into multiple local regions, and each region is processed independently with its own transfer function parameters. This segmentation allows contrast enhancement to be applied locally rather than globally, preventing the amplification of noise and compression artifacts across the entire image while still improving contrast in specific regions where it is needed.
Solution Approach 2:
Different transfer function parameters are applied to different local regions of the image based on their specific characteristics. This local quality approach ensures that contrast enhancement is adapted to each region's needs, improving overall image quality while avoiding the uniform application of enhancement that would exacerbate noise and artifacts throughout the entire image.
2Quantity of substance
If saturation is increased uniformly across the image, then image vibrancy is improved, but skin tones and natural colors become unnatural
Solution Approach 1:
The transfer function applies different saturation adjustments to different local regions and color ranges. By analyzing the hue and saturation values in each region, the system selectively enhances saturation only where appropriate while preserving natural skin tones and colors, thus maintaining color accuracy while improving overall vibrancy.
Solution Approach 2:
The transfer function dynamically adjusts saturation parameters based on local image characteristics, including hue, saturation, and brightness values. This parameter change strategy allows the system to increase saturation in regions where it enhances vibrancy while avoiding excessive saturation in regions where it would cause unnatural color appearance.
3Illumination intensity
If contrast enhancement is applied globally, then perceived contrast is improved, but memory requirements and computational complexity increase
Solution Approach 1:
The image processing is segmented into multiple local regions, each with its own simplified transfer function parameters. This segmentation reduces the overall computational complexity and memory requirements compared to processing the entire image with a single complex transfer function, while still achieving improved perceived contrast through localized adjustments.
Solution Approach 2:
Instead of applying full contrast enhancement across the entire image, the system applies partial enhancement to specific local regions where it is most needed. This partial action approach reduces the overall computational load and memory requirements while still achieving the desired perceived contrast improvement in the most critical areas.
Data Source
AI summary
In accordance with the teachings of the present invention, a system and method for local value adjustment are provided. In one embodiment, the method includes identifying a hue, saturation, and brightness value for each pixel of an image, determining whether the hue and saturation for each pixel fall within a first predetermined set of hue and saturation combinations, determining whether the brightness value for each pixel of the image falls within a predetermined set of brightness values, and selectively applying a gain to the saturation of each pixel based upon the determination of whether the hue and saturation value of the pixel falls within the first predetermined set of hue and saturation combinations and the determination of whether the brightness value of the pixel falls within the predetermined set of brightness values. The method further comprises decimating the image into multiple localized regions, determining a mean brightness value for each localized region, determining whether the hue and saturation for each pixel fall within a second predetermined set of hue and saturation combinations, and selectively increasing the perceived contrast of the image around the mean brightness value for each localized region by applying a transfer function to each pixel of the image based upon the determination of whether the hue and saturation of the pixel fall within the second predetermined set of hue and saturation combinations.