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In this issue: Image Integration Toolbox Using photospace to design an exposure algorithm: Use measured distributions of picture taking preferences to maximize your customer’s likelihood of achieving high quality images Image Integration at EI2008: Full copies of paper and slides are now available. |
![]() Tools and the Image Integration Toolbox: Several years ago I worked at Polaroid with a young colleague on improving the image quality of new digital photo print media. Although time was always in short supply I suggest spending a good proportion of it on creating tools, since a good tool facilitates the solution of more problems than those the tool was originally intended for. One day she came into my office and wrote the characters shown above on my whiteboard |
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The Confucian proverb says:
If a workman wishes to improve his work, he must
first sharpen his tools We have introduced the first tool, Image Phi, which we demonstrated at the 2008 Electronic Imaging Conference in San Jose. Please check out the description of Image Phi at our website. Using Photospace to design and optimize an exposure algorithm: Using
Image Phi, we have measured the frequency of mobile phone images over the
operational space — Subject Illumination and Subject-Camera Distance. Since the
majority of images are made under low subject illumination, the low light
performance of mobile camera phones is a prime design consideration. In addition
to the primary camera characteristics — lens f/#, flash type, flash power and
pixel area — the algorithm that balances ambient and flash illumination with
effective ISO speed is also of great interest. The optimization depends on
balancing camera-subject motion blur with image noise. In general, shutter speed
required for a correct exposure varies inversely with the ambient subject
illumination. At low-light conditions the shutter duration increases the
negative effects of motion blur and dark current. Decreasing the shutter speed
requires the increase of effective ISO speed, which is accompanied by increased
noise. Other constraints are imposed by the desire to have a mixture of ambient
and flash illumination [‘fill flash’] and the rapid fall-off of the flash
component with distance. The goal is designing a system that produces the
largest number of high quality images for the conditions that your customer
prefers.We have developed a digital still camera model that can be populated with typical characteristics of mobile camera phones to predict image quality for a given set of subject lighting and subject-camera distances. Extending the evaluation over the usage distribution [photospace] will yield a predicted average image quality [an average weighted by the photospace distribution of typical mobile phone camera usage]. We have devised simple exposure algorithms that can be applied to the limited on-camera flash capabilities of mobile phone cameras in order to determine the optimum shutter durations for ambient and flash exposures under different camera operating conditions. In the example shown below, we have assumed that the camera has autofocus capability and a small, quenched xenon flash. ![]() If you feel that applications such as described in the example above would aid your product development, or if you would prefer assistance in determining your customer’s image usage patterns and their subjective assessment of image quality, please contact us — bror.hultgren@i-2-q.com Image Integration at Electronic Imaging 2008: |
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