Visible SNR metric calculation

We calculate the visible SNR (VSNR) of a camera and its processing pipeline. This metric can be used to evaluate different imaging artifacts. See Farrell et al., 2010.

We show both the VSNR and the $\Delta E$ CIELAB error.

The script relies on the camera object a structure with slots for optics (oi), image sensor (sensor), and image processing pipeline (vci).

See also: cameraVSNR, cameraCreate, s_metricsAcutance, s_metricsColorAccuracy

Copyright ImagEval Consultants, LLC, 2012.



Initialize the virtual camera.

camera = cameraCreate;
Camera from default oi,sensor and ip
Interpolating display SPD for consistency with new wave.

Visible SNR (VSNR) at different mean intensities

% Light intensity of uniform field in cd/m2
levels = logspace(1.5,3,3);

% Computes for a 10 ms exposure duration.
cVSNR  = cameraVSNR(camera,levels);

xlabel('Light level (cd/m^2)')
ylabel('vSNR (1/dE)');
grid on;
Warning: Resizing sensor to match scene FOV (5.0) 
Warning: Resizing sensor to match scene FOV (5.0) 
Warning: Resizing sensor to match scene FOV (5.0) 

It can be nice to plot the dE values on the right hand side.

ax = plotyy(cVSNR.lightLevels,cVSNR.vSNR,cVSNR.lightLevels,cVSNR.vSNR,'loglog');
xlabel('Light level (cd/m^2)')
grid on;
vsnrLabels = get(ax(1),'yticklabel');

deLabels = vsnrLabels;
for ii=1:length(vsnrLabels)
    deLabels{ii}   = num2str( round(100*(1/str2double(vsnrLabels{ii})))/100);
    vsnrLabels{ii} = num2str( round(100*(str2double(vsnrLabels{ii})))  /100);

set(get(ax(2),'Ylabel'),'String','\Delta E','fontsize',20)