LiYuXie

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

Image Quality Metrics

Nowadays, a lot of metrics are used to perceive image quality, which can be found in the paper Development of the I3A CPIQ spatial metrics[1]. Their goals are to predict the visible differences between a pair of images. These metrics are commonly used to measure the important aspects of image quality such as sharpness, noise, contrast and so on.

visual SNR

The vSNR metric can be used in simulations to predict how imaging system components affect noise visibility[2].

References

[1] Baxter, Donald, et al. "Development of the I3A CPIQ spatial metrics." IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2012.

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