LiYuXie

Project Title

Camera Image Quality Metrics

Introduction

ISO has a set of camera image quality metrics to quantify resolution, noise, color accuracy of cameras. The metrics are a set of values, but the effectiveness of these metrics might not be good for evaluation of real camera images, observed by human eyes. As a result, We need to evaluate the metrics by comparing the metrics and human observation.

Through ISET, we can calculate the main metric to evaluate visual noise of a camera – vSNR. vSNR is the ratio of signal power to noise power, calculated from XYZ value of the processed image. By varying the Luminance, pixel size, and optical/sensor parameters, we evaluate whether the vSNR can match the observation of eyes or not for specific scenes, e.g. face, text, scenery.

Background

A lot of metrics are used to measure the important aspects of image quality such as sharpness, signal to noise ratio(SNR), and so on. However, even the most commonly used metrics today, e.g., sharpness and SNR, do not in many cases correlate very well with the perceived image quality. The vSNR metric can be used in simulations to predict how imaging system components affect noise visibility[1].

References

J. Farrell et al., ”Using visible SNR (vSNR) to compare the image quality of pixel binning and digital resizing”, Proc SPIE 7537, 75370C (2010).