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 visible 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.

visible SNR

Signal-to-noise ratio(SNR)is a measure that compares the level of a signal to the level of background noise. It is defined as the ratio of signal power to the noise power. The SNR used in imaging is a physical measure of the sensitivity of a imaging system.

The vSNR is the inverse of the standard deviation of the SCIELAB representation of a uniform field; its units are 1/ΔE[2].The vSNR metric can be used in simulations to predict how imaging system components affect noise visibility[2].

Results

Luminance

Human Face

FoV=5, F-number=5, View Distance=1, Pixel Size=0.3

File:L1.PNG

light level of the scene from 5 to 1000

File:L2.PNG

FoV=5, F-number=5, View Distance=1, Pixel Size=1

File:L3.JPG

light level of the scene from 5 to 1000

File:L4.JPG

FoV=5, F-number=5, View Distance=1, Pixel Size=2

File:L5.JPG

light level of the scene from 5 to 1000

File:L6.JPG

Text

FoV=5, F-number=5, View Distance=1, Pixel Size=0.3

File:T1.JPG

light level of the scene from 5 to 1000

File:T2.JPG

FoV=5, F-number=5, View Distance=1, Pixel Size=1

File:T3.JPG

light level of the scene from 5 to 1000

File:T4.JPG

FoV=5, F-number=5, View Distance=1, Pixel Size=2

File:T5.JPG

light level of the scene from 5 to 1000

File:T6.JPG

Landscape

FoV=5, F-number=5, View Distance=1, Pixel Size=0.3

File:S1.JPG

light level of the scene from 5 to 1000

File:S2.JPG

FoV=5, F-number=5, View Distance=1, Pixel Size=1

File:S3.JPG

light level of the scene from 5 to 1000

File:S4.JPG

FoV=5, F-number=5, View Distance=1, Pixel Size=2

File:S5.JPG

light level of the scene from 5 to 1000

File:S6.JPG

Pixel Size

Human Face

FoV=5, F-number=5, View Distance=1, Light Level = 226

File:PF1.JPG

Pixel Size = 0.3, 1, 2, 4 The accordingly VSNR = 0.1, 0.1, 0.85

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).