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].

Methods

Software

Matlab 7.14

Algorithms

To evaluate the effectiveness of vSNR for measuring image quality, we investigate vSNR effectiveness with different parameters for processing different type of scenes. We use ISET to calculate metrics for different parameters (luminance, pixel size, F-number, field of view, and view distance). We also use ISET to simulate how camera captures and processes different type of scenes (e.g. faces, text and landscapes). For each condition, we compare the metrics with the appearance of these images as they are rendered on a display. We use control variate method to measure and compare the influence of each variable.

Experimental Procedure

(1) Luminance
(i) For human face,
Control Fov=5, f#=5, viewD=1, pixel=0.5. Change light level, lightLevels = [5,10,100,200]
Control Fov=5, f#=5, viewD=1, pixel=1. Change light level, lightLevels = [5,10,100,200]
Control Fov=5, f#=5, viewD=1, pixel=2. Change light level, lightLevels = [5,10,100,200]

(ii) For text,
Control Fov=5, f#=5, viewD=1, pixel=0.5. Change light level, lightLevels = [5,10,100,200]
Control Fov=5, f#=5, viewD=1, pixel=1. Change light level, lightLevels = [5,10,100,200]
Control Fov=5, f#=5, viewD=1, pixel=2. Change light level, lightLevels = [5,10,100,200]

(iii) For landscape,
Control Fov=5, f#=5, viewD=1, pixel=0.5. Change light level, lightLevels = [5,10,100,200]
Control Fov=5, f#=5, viewD=1, pixel=1. Change light level, lightLevels = [5,10,100,200]
Control Fov=5, f#=5, viewD=1, pixel=2. Change light level, lightLevels = [5,10,100,200]

(2) Pixel Size
(i) For human face,
Control light level = 226, Fov=5, f#=5, viewD=1. Change pixel = [0.3, 1, 2, 4].

(ii) For text,
Control light level = 226, Fov=5, f#=5, viewD=1. Change pixel = [0.3, 1, 2, 4].

(iii) For landscape,
Control light level = 226, Fov=5, f#=5, viewD=1. Change pixel = [0.3, 1, 2, 4].

(3) Field of View (Fov)
(i) For human face,
Control f#=5, viewD=1, light level = [51, 110, 226]. Change FoV = [3, 5, 8].

(ii) For text,
Control f#=5, viewD=1, light level = [51, 110, 226]. Change FoV = [3, 5, 8].

(iii) For landscape,
Control f#=5, viewD=1, light level = [51, 110, 226]. Change FoV = [3, 5, 8].

(4) F-number
(i) For human face,
Control Fov=5, viewD=1, light level = [51, 110, 226]. Change f# = [3, 5, 8].

(ii) For text,
Control Fov=5, viewD=1, light level = [51, 110, 226]. Change f# = [3, 5, 8].

(iii) For landscape,
Control Fov=5, viewD=1, light level = [51, 110, 226]. Change f# = [3, 5, 8].

(5) View Distance
For human face,
Control Fov=5, f#=5, pixel=0.7, lightLevels = logspace(0.1,3,10). Change viewD = [0.2, 3.2].

Results

Luminance

Human Face

Fov=5, f#=5, viewD=1
Horizontally (from left to right): lightLevels = [5,10,100,200]
Vertically (from top to bottom): pixel = [0.5,1,2,4]


From the image displayed, we could see that image quality is getting better with increasing light level in each pixel size condition.

vSNR plot:


From the above plot we could see that vSNR increases with increasing light level in each pixel size condition. Thus, we could conclude that vSNR can effectively reflect the noise level and image quality of human face, in terms of light level.

Text Human Face

Fov=5, f#=5, viewD=1
Horizontally (from left to right): lightLevels = [5,10,100,200]
Vertically (from top to bottom): pixel = [0.5,1,2,4]


From the image displayed, we could see that image quality is getting better with increasing light level in each pixel size condition.

vSNR plot:


From the above plot we could see that vSNR increases with increasing light level in each pixel size condition. Thus, we could conclude that vSNR can effectively reflect the noise level and image quality of human face, in terms of light level.

Landscape Human Face

Fov=5, f#=5, viewD=1
Horizontally (from left to right): lightLevels = [5,10,100,200]
Vertically (from top to bottom): pixel = [0.5,1,2,4]


From the image displayed, we could see that image quality is getting better with increasing light level in each pixel size condition.

vSNR plot:


From the above plot we could see that vSNR increases with increasing light level in each pixel size condition. Thus, we could conclude that vSNR can effectively reflect the noise level and image quality of human face, in terms of light level.

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.

Text

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

File:PT1.JPG

Pixel Size = 0.3, 1, 2, 4. The accordingly VSNR are all are all 0.04.

Landscape

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

File:PL1.JPG

Pixel Size = 0.3, 1, 2, 4. The accordingly VSNR are all are all 0.07.

Field of View (FoV)

Human Face

F-number=5, View Distance=1

File:FVF1.JPG

FoV = [3, 5, 8], Light Level = [51, 110, 226]

File:FVF2.JPG

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).