YCbCr Image Correction for Low-Light Heart Rate Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating heart rate using remote photoplethysmography (rPPG) struggle in low-illumination environments due to difficulties in extracting heart rate signals from images with low visibility, leading to color distortion and inaccurate heart rate detection.
Innovation Solution
A method involving the conversion of an RGB image to YCbCr space, calculating a weighted value using a learning model, and generating a corrected image to enhance illumination and reduce noise, followed by heart rate estimation from the corrected image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If contrast ratio is increased to improve visibility of dark region, then illumination is improved, but color distortion and excessive saturation improvement cause deterioration of color signal
Solution Approach 1:
The image processing is segmented into distinct components: luminance correction through learning model and chrominance preservation through color space conversion. This allows independent optimization of illumination improvement while maintaining color signal integrity for rPPG analysis.
Solution Approach 2:
The learning model applies localized weighted values to different regions of the image based on their specific illumination conditions. The YCbCr color space conversion preserves chrominance information locally in regions requiring correction, ensuring that color distortion does not propagate throughout the entire image.
2Ease of operation
If conventional image enhancement is applied to low-illumination image, then visibility is improved, but heart rate detection accuracy deteriorates due to color distortion
Solution Approach 1:
The YCbCr color space acts as an intermediary representation that separates luminance and chrominance information. This allows the learning model to enhance visibility through luminance correction while the chrominance components remain preserved for accurate heart rate detection through rPPG analysis.
Solution Approach 2:
The invention changes the parameter representation from RGB to YCbCr color space, allowing independent manipulation of luminance (Y) and chrominance (CbCr) parameters. This parameter transformation enables visibility improvement through weighted luminance adjustment without affecting the chrominance parameters needed for heart rate detection.
3Reliability
If learning model is used to correct low-illumination effect, then color signal quality is improved, but device complexity increases
Solution Approach 1:
The invention replaces complex mechanical or hardware-based illumination correction systems with a software-based learning model that processes images computationally. This substitution achieves superior color signal quality through intelligent algorithms while reducing physical device complexity and cost.
Data Source
AI summary
Disclosed in one embodiment of the present invention is a method for estimating a heart rate on the basis of a corrected image, the method comprising the steps of: acquiring a serial image; extracting a region of interest (ROI) from the acquired serial image; converting, from an RGB space to a YCbCr space, the color space of the extracted ROI; calculating a weighted value applied to the serial image, by inputting the converted result to a learning model; applying the calculated weighed value to the serial image and generating a serial image with the ROI corrected; and estimating the heart rate of a person included in the corrected serial image, by analyzing the ROI of the corrected serial image.