Low-Light Pixel Color Correction Using YUV Chrominance Selection
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Solution Overview
Problem
Conventional image processing techniques fail to effectively correct abnormal reddish tints in black regions of images captured in low-light conditions, leading to high false positive and negative rates, and often result in color degradation due to amplified color mismatch by image signal processors.
Innovation Solution
A method and apparatus that operate on image data in a YUV color space to detect and correct color tinted pixels by replacing them with corresponding pixels from an earlier stage in the image processing pipeline, using reference chrominance data for grey pixels and current chrominance data for color tinted pixels, and performing tone mapping and gamma correction operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If digital gain or dynamic gain compression is performed by the image signal processor to amplify signal in low-light conditions, then the signal strength is improved, but the color mismatch is amplified resulting in visible reddish tints in black regions
Solution Approach 1:
The patent applies black level correction before tone mapping operations to pre-establish accurate black levels and prevent color tint amplification in subsequent processing stages. This preliminary correction ensures that grey pixels remain grey throughout the processing pipeline, preventing the reddish tint problem in low-light images.
Solution Approach 2:
The patent introduces an intermediary detection mechanism that identifies grey pixels by checking if U and V chrominance values are within a threshold range of midpoint values. This intermediary detection step allows the system to selectively apply corrections only where needed, preventing false positives and negatives while maintaining image quality.
2Manufacturing precision
If conventional techniques process raw image data to correct color tints, then correction is applied, but false positive and negative rates are high resulting in color degradation
Solution Approach 1:
The patent replaces conventional raw image data processing with YUV color space processing. By transforming the image data to YUV space and operating on the U and V chrominance components, the system achieves more reliable grey pixel detection and correction with significantly reduced false positive and negative rates compared to conventional RGB-based methods.
Solution Approach 2:
The patent changes the parameter space from raw RGB values to YUV color space parameters. By detecting grey pixels based on U and V values being close to midpoint values, and by applying corrections in this transformed parameter space, the system achieves higher reliability and lower error rates in color correction.
Data Source
AI summary
Aspects of the present disclosure relate to color correction in image processing pipelines. An example method may include receiving first image data corresponding to reference luminance data and reference chrominance data for each of a plurality of pixels, determining that the first image data corresponds to a raw image captured in a dark environment, generating second image data by performing one or more tone mapping operations on the first image data, the second image data corresponding to current luminance data and current chrominance data for each of the plurality of pixels, and generating output image data. For each pixel of the plurality of pixels, the output data may include an output luminance value of a corresponding pixel of the current luminance data, and chrominance values of the corresponding pixel from a selected one of the reference chrominance data and the current chrominance data, the selection based at least in part on the reference chrominance data and the current chrominance data.