Chroma Correction in SDR-to-HDR Inverse Gamut Mapping
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Solution Overview
Problem
Conventional methods for converting Standard Dynamic Range (SDR) images to High Dynamic Range (HDR) images face challenges in maintaining color accuracy and reducing visual artifacts such as noise in dark regions and banding in bright regions, while expanding brightness and color information beyond the original capture range.
Innovation Solution
The method involves chroma correction techniques in inverse gamut mapping (IGM) that include intensity transformation, chroma scaling, hue rotation, and chroma mapping operations, along with gamut boundary descriptor generation, to convert SDR images to HDR images, ensuring accurate color adjustment and minimizing perceptible artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional HDR/WCG converters are used to convert SDR content to HDR format, then HDR broadcast needs are met, but color accuracy deteriorates and visual artifacts increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting chroma scaling factors and hue rotation angles based on the intensity values of image pixels. Different chroma scaling factors are applied to different intensity ranges (dark, mid-tone, bright regions), and hue rotation is applied selectively to maintain color fidelity while expanding gamut. This resolves the contradiction by adapting conversion parameters to local image characteristics rather than using a fixed conversion approach.
Solution Approach 2:
The patent implements local quality by processing different regions of the image with different conversion parameters. Dark regions use one set of chroma scaling factors, mid-tone regions use another, and bright regions use yet another. This allows each region to be optimized independently for its specific visual characteristics, maintaining color accuracy where possible while expanding gamut where beneficial, thereby resolving the contradiction between HDR compliance and color accuracy.
2Illumination intensity
If brightness and color information are expanded beyond original capture range, then HDR visual quality improves, but noise in dark regions and banding in bright regions increases
Solution Approach 1:
The patent uses parameter changes by applying different chroma scaling factors corresponding to different intensity ranges. For dark regions, smaller chroma scaling factors are applied to prevent noise amplification, while for bright regions, larger factors are applied to enhance color saturation without creating excessive banding. The hue rotation angle is also adjusted based on intensity to optimize color representation. This resolves the contradiction by adapting chroma expansion parameters to the specific intensity range being processed.
Solution Approach 2:
The patent implements dynamics by making the chroma scaling factors and hue rotation angles variable rather than fixed. The conversion parameters dynamically adjust based on the intensity values of each pixel or region, allowing the system to adaptively control the expansion of brightness and color information. This dynamic adjustment prevents uniform over-expansion that would cause noise and banding, while still achieving the desired HDR visual quality improvement.
3Manufacturing precision
If inverse gamut mapping is applied to expand color gamut, then color fidelity enhances, but primary colors become misaligned
Solution Approach 1:
The patent applies parameter changes by using intensity-dependent hue rotation angles and chroma scaling factors. The hue rotation is not applied uniformly but is adjusted based on the intensity values, which helps maintain proper color alignment across different brightness levels. This resolves the contradiction by making the gamut expansion process adaptive to local intensity characteristics rather than applying a uniform transformation.
Solution Approach 2:
The patent implements dynamics by making the hue rotation angle and chroma scaling factors variable parameters that adapt to the intensity values of image pixels. This dynamic approach allows the system to maintain primary color alignment while still expanding the color gamut, as the parameters adjust to preserve the relationships between primary colors at different intensity levels.
Data Source
AI summary
Chroma correction of inverse gamut mapping (IGM) for standard dynamic range (SDR) to high dynamic range (HDR) image conversion includes: converting R,G,B color components in the RGB color format of a pixel of an image to an intensity component (I) and chroma components (Ct and Cp) of an ICtCp color format, wherein the R,G,B color components represent red, green, and blue colors; applying an intensity transformation operation on the intensity component (I) of the pixel; executing a chroma correction operation on the transformed intensity component (I) and the chroma components (Ct and Cp) of the pixel; and converting the intensity component (I) and the chroma components (Ct and Cp) of the pixel back to the RGB color format.