Image fidelity

Image fidelity, often referred to as the ability to discriminate between two images[1] or how closely the image represents the real source distribution.[2] Different from image quality, which is often referred to as the subject preference for one image over another, image fidelity represents to the ability of a process to render an image accurately, without any visible distortion or information loss. The two terms are often used interchangeably, but they are not the same.[3]

Naturally, there is confusion between image fidelity and image quality. Image quality, for example, if we cannot detect the difference between a photograph and a digitally printed image, we might conclude that the digital print has photographic image quality.[4] But subjective impressions of image quality are much more difficult to characterize and, consequently, nearly impossible to quantify. It is not difficult to demonstrate that people use multiple visual factors or dimensions in complex non-linear combinations to make judgements about image quality.[5] There are also significant individual differences in their judgements.[6]

Factors of image fidelity in photography

In photography, image fidelity is also referred to Micro-contrast or 3D Pop. The inner tonal rendition of an image could be found as more shades and details are rendered.

To increase the Image Fidelity, there are three ways to realize it.[7]

First is to adapt a high transmission lens on a camera. Lenses with high transmissive characters could direct more lights into the sensor.

Second is to increase the sensor saturation. There are two ways to realize: first is to reduce the thickness of the filter array on the camera sensor so that more low-gain lights could be recorded; second is to increase the light on the subject so that the sensor is more saturated. The latter is more often. Flash is usually the way to achieve it.

Third is reducing or removing the color filter array on a camera sensor. To remove or reduce the thickness of the filter array on the camera sensor so that more low-gain lights could be recorded.[8] This process is also called "Debayering a sensor".[9]

References
  1. Silverstein, D. A.; Farrell, J. E. (September 1996). "The relationship between image fidelity and image quality". Proceedings of 3rd IEEE International Conference on Image Processing. 1: 881–884 vol.1. doi:10.1109/ICIP.1996.559640. ISBN 0-7803-3259-8. S2CID 17711937.
  2. "2000ASPC..217..344W Page 344". articles.adsabs.harvard.edu. Bibcode:2000ASPC..217..344W. Retrieved 2020-12-03.
  3. Silverstein, D. A.; Farrell, J. E. (September 1996). "The relationship between image fidelity and image quality". Proceedings of 3rd IEEE International Conference on Image Processing. 1: 881–884 vol.1. doi:10.1109/ICIP.1996.559640. ISBN 0-7803-3259-8. S2CID 17711937.
  4. Silverstein, D. A.; Farrell, J. E. (September 1996). "The relationship between image fidelity and image quality". Proceedings of 3rd IEEE International Conference on Image Processing. 1: 881–884 vol.1. doi:10.1109/ICIP.1996.559640. ISBN 0-7803-3259-8. S2CID 17711937.
  5. "IMAGE QUALITY: A MULTIDIMENSIONAL PROBLEM" (PDF). NASA.
  6. Multidimensional Scaling: Theory and Applications in the behavioral sciences. New York: Seminar Press. 1972. pp. 105–156. ISBN 978-0127857817.
  7. "The FACTS of IMAGE FIDELITY & its existence". Youtube.
  8. Design, Wild Dog (2017-09-22). ""Monochroming" a colour sensor and colour photography with the Monochrom - Wild Dog Design". Retrieved 2020-12-03.
  9. Design, Wild Dog (2017-09-22). ""Monochroming" a colour sensor and colour photography with the Monochrom - Wild Dog Design". Retrieved 2020-12-03.

Further reading
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.