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| In the Protoype Phase, actual devices are available for
evaluation, but quality is often defect limited. Image Integration offers model based metrics to predict Image Quality
from primitive measurements of the imaging system performance.
For example, micro image quality is a primary constituent of
over all image quality and is represented by the image sharpness
and noise. Existing measurement and analysis packages produce
objective measurements of these attributes. Image Integration
offers tools and metrics that function with these measurements
to provide correlates of the subjective assessment of these
attributes. |
Spatial Frequency Response (SFR): SFR is an objective
measurement of the reproduction of fine image detail. Subjective
Quality Factor (SQF) is a visually weighted measure of
information content that is highly correlated with perceived
quality.
Noise Power Spectrum (NPS): NPS is an objective
measurement of the image non-uniformity at all scales. Visual
granularity is a visually weighted measure of perceived grain
derived from NPS that is highly correlated with perceived
quality.
Micro Image Quality (μIMQ): Image quality is computed from SQF and visual granularity
using a Minkowski metric that accounts for the masking of
sharpness by grain. |
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