Image Quality Factors | Imatest (2024)

Overview of image quality and Imatest measurements

Image quality is one of those concepts that’s greater than the sum of its parts. But you can’t ignore the parts if your goal is to produce images of the highest quality. Every image quality factor counts.

This page introduces the key image quality factors and briefly describes how Imatest™ measures them— with links to detailed pages. It is a guide to Imatest organized by image quality factors. Other guides include the Tour (organized by module) and Imatest documentation (the Table of Contents).

To illustrate the quality factors, we use this early morning image of Monument Valley from Hunt’s Mesa, near the Arizona-Utah border.

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.