The use of vision-based image quality metrics to predict low-light performance of camera phones

B. Hultgren; D. Hertel

doi:10.1117/12.838920

18 January 2010 The use of vision-based image quality metrics to predict low-light performance of camera phones

B. Hultgren, D. Hertel

Proceedings Volume 7529, Image Quality and System Performance VII; 75290E (2010) https://doi.org/10.1117/12.838920
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

Small digital camera modules such as those in mobile phones have become ubiquitous. Their low-light performance is of utmost importance since a high percentage of images are made under low lighting conditions where image quality failure may occur due to blur, noise, and/or underexposure. These modes of image degradation are not mutually exclusive: they share common roots in the physics of the imager, the constraints of image processing, and the general trade-off situations in camera design. A comprehensive analysis of failure modes is needed in order to understand how their interactions affect overall image quality. Low-light performance is reported for DSLR, point-and-shoot, and mobile phone cameras. The measurements target blur, noise, and exposure error. Image sharpness is evaluated from three different physical measurements: static spatial frequency response, handheld motion blur, and statistical information loss due to image processing. Visual metrics for sharpness, graininess, and brightness are calculated from the physical measurements, and displayed as orthogonal image quality metrics to illustrate the relative magnitude of image quality degradation as a function of subject illumination. The impact of each of the three sharpness measurements on overall sharpness quality is displayed for different light levels. The power spectrum of the statistical information target is a good representation of natural scenes, thus providing a defined input signal for the measurement of power-spectrum based signal-to-noise ratio to characterize overall imaging performance.

Citation Download Citation

B. Hultgren and D. Hertel "The use of vision-based image quality metrics to predict low-light performance of camera phones", Proc. SPIE 7529, Image Quality and System Performance VII, 75290E (18 January 2010); https://doi.org/10.1117/12.838920

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KEYWORDS

Cameras

Image quality

Signal to noise ratio

Stochastic processes

Spatial frequencies

Surface plasmons

Image processing

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