Which of the following factors has the greatest effect on the brightness of the digital image

Image Quality Factors are also called Key Performance Indicators (KPIs).

Image Quality Factor Video Series

Image quality measurements are affected by the

• Lens — Imatest cannot measure lenses by themselves, but lenses can be effectively compared to one another using a single camera body with consistent image processing settings.
• Sensor — Imatest can measure the performance of the Lens+sensor from minimally-processed RAW images if they are available. Sharpness, distortion, vignetting, Lateral Chromatic Aberration, noise, and dynamic range are the principal factors that can be measured at this stage. Most of these measurements can be clearly classified as good/bad.
• Image processing pipeline— typically includes demosaicing, color correction, white balance, application of gamma and tonal response curves, sharpening, and noise reduction. Measured from the image delivered to the user (such as in-camera JPEG images). Additional image quality factors include tonal response (contrast, etc.), color response, and many others. The output of the pipeline may be compared to the minimally-processed lens+sensor measurements.The effect of the pipeline on subjective image quality can be highly scene- and application-dependent, making it difficult to assign “good” or “bad” rankings. Imatest results for these factors need to be interpreted carefully. Examples:
  • Higher contrast images often score higher in visual quality assessment tests, but high contrast can cause clipping (visible as burnt-out highlights) in contrasty (often sunlit) scenes. Tonal response curves with “shoulders” can help. See Stepchart and Multicharts.
  • Most consumers find accurate color to be boring, so camera manufacturers “enhance” colors in a number of ways, such as boosting chroma. See Colorcheck and Multicharts.
  • Most consumers dislike noisy images, but software noise reduction (typically lowpass filtering in areas that lack contrasty features) can remove fine texture, resulting in a cartoon-like “plastic” skin appearance. See Log F-Contrast and Random/Dead Leaves.

Summary table

This table summarizes the image quality factors and links to pages where they are described in detail. Most of the charts are available from Imatest Store.

Notes: [1] Not available in Imatest Studio. Available in Master, Image Sensor, etc. [2] can be printed from Test charts, but we recommend purchasing it from the Imatest Store.

Links

The RIT Center for Imaging Science has done some excellent work, but links that used to be on their site are hard to find. Some missing links:  Direct Digital Image Capture of Cultural Heritage from RIT is a gold mine of information. Links to a number of reports on image quality targeted at museums and cultural institutions. The 78-page Final Project Report by Berns, Frey, Rosen, Smoyer and Taplin, July 2005, is probably the best summary unless you have time for Erin P (Murphy) Smoyer’s 345 page Master’s thesis (about twice the length of the average Ph.D. thesis).

The Research Library Group (RLG) has some useful documents such as Guides to Quality in Visual Resource Imaging (2000). These articles are the predecessors to the above-mentioned RIT Direct Digital Image Capture work.

Paul van Walree has an excellent page on Optics, covering several sources of degradation.

The University of Texas Laboratory for Image & Video Engineering is doing some interesting work on image and video quality assessment, which approaches the problem using information theory, natural scene statistics, wavelets, etc. Challenging material!

Details of several Imatest algorithms are included in Appendix C, Video Acquisition Measurement Methods (pp. 91-125), of the Public Safety SoR (Statement of Requirements) volume II v 1.0, released by SAFECOM, prepared by ITS (a division of NTIA, U.S. Department of Commerce). No credit is given, but the style and illustrations will be recognizable.