We introduce a new metric, the visible signal-to-noise ratio (vSNR), to analyze how pixel-binning and resizing methods
influence noise visibility in uniform areas of an image. The vSNR is the inverse of the standard deviation of the SCIELAB
representation of a uniform field; its units are 1/ΔE. The vSNR metric can be used in simulations to predict
how imaging system components affect noise visibility. We use simulations to evaluate two image rendering methods:
pixel binning and digital resizing. We show that vSNR increases with scene luminance, pixel size and viewing distance
and decreases with read noise. Under low illumination conditions and for pixels with relatively high read noise, images
generated with the binning method have less noise (high vSNR) than resized images. The binning method has
noticeably lower spatial resolution. The binning method reduces demands on the ADC rate and channel throughput.
When comparing binning and resizing, there is an image quality tradeoff between noise and blur. Depending on the
application users may prefer one error over another.
We describe a method for simulating the output of an image sensor to a broad array of test targets. The method uses a modest set of sensor calibration measurements to define the sensor parameters; these parameters are used by an integrated suite of Matlab software routines that simulate the sensor and create output images. We compare the simulations of specific targets to measured data for several different imaging sensors with very different imaging properties. The simulation captures the essential features of the images created by these different sensors. Finally, we show that by specifying the sensor properties the simulations can predict sensor performance to natural scenes that are difficult to measure with a laboratory apparatus, such as natural scenes with high dynamic range or low light levels.
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