Digital imaging technology has today become ubiquitous. Digital cameras are finding uses in an ever increasing number of products, in everything from endoscopy equipment to consumer cameras and cars. Thus, a growing number of engineers are becoming exposed to camera systems in one way or the other. Even though many new applications of cameras are aimed at machine vision applications, there is still a wide and growing range of uses for cameras delivering images for human vision. For instance, in automotive applications there are also imaging systems presenting images on displays. Therefore, the need for image quality metrics delivering perceptually correlated results is continuously expanding. Furthermore, a systematic and reproducible as well as accurate methodology must be developed in order to guarantee usable results. An important part of this is the use of standardized image quality assessment methods.

One can often hear that it is not possible to make an assessment of image quality because of its “subjective” nature. The aim of this course is to show that the opposite is true, and to provide methods to perform this assessment. To obtain the most accurate determination of image quality, a statistical treatment of results from subjective experiments is in principle necessary. The drawback of this methodology is that it can be quite time and resource consuming and therefore expensive. In contrast, objective measurements of image quality can be fast and automated. The drawback is that many times such methods give results that do not reflect the actual perception of image quality. In this course, objective methods that do provide good correlation with subjective experiments will be presented and ways to design custom methods that correlate with subjective results will be discussed. Additionally, many of the objective methods discussed may be used in applications where humans are not intended to observe the images, i.e., machine vision. 

On-Chip and 3D Interconnects
On-Chip and 3D Interconnects


The course starts with defining image quality and presenting a set of image quality attributes, combinations of which will determine the overall perceived quality of a certain image. Each individual attribute will be explained in detail. Subjective image quality assessment will then be described with focus on some commonly encountered methods. This will end up in presenting an efficient way to perform subjective experiments in a way that is reproducible and comparably quick; the image quality ruler method. An application of this method to video will also be discussed.

The course then continues with a detailed discussion of the camera and its components: the lens, image sensor, and image processing. Next, the most extensive part of the course follows, with explanations of objective measurement methods for assessing the degradations of the image quality attributes previously defined. Among these attributes are sharpness, noise, color, etc. Then, methods to make the link between the results of the objective measurements and results from subjective experiments are presented. In order to perform accurate measurements, they must be performed under controlled conditions. 

The next part therefore provides a discussion of how to set up an image quality testing lab, including design of the lab space, measurement equipment, test charts, etc. The course then ends with a discussion around experimental conditions and protocols.



Image scientists, photographers, engineers, or managers who want to learn more about image quality and how to evaluate still as well as video cameras for different applications.
On-Chip and 3D Interconnects

Day 1

1. Image Content and Image Quality 

  1. Image Interpretation and Visualization of Objects 
  2. Defining Image Quality 
  3. Image Quality Attributes 
  4. Subjective vs Objective Image Quality Assessment  

2. Subjective Image Quality Assessment  

  1. Psychophysics 
  2. Measurement scales 
  3. Methods 
  4. Paired comparison, Thurstone scaling 
  5. Category scaling, MOS 
  6. Image quality ruler 
  7. Video quality assessment 
  8. Video quality ruler 

3. Radiometry and Photometry

4. Image Formation

  1. Principle of the Camera 
  2. The Lens 
  3. The Image sensor 
  4. Image Signal Processing 
  5. Video Processing
  6. Flash and Illumination

Day 2

5. Objective Image Quality Metrics 

  1. Sharpness 
  2. Texture Blur 
  3. Color 
  4. Noise
  5. Dynamic Range
  6. Exposure and Tone 
  7. Distortion 
  8. Color Fringing 
  9. Shading
  10. Stray Light
  11. Video Metrics 

6. Subjectively Correlated Image Quality Metrics

7. Image Lab Design

7. Experimental Considerations

8. Summary and Conclusions