Automation Image Comparison Testing
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The document discusses automating image comparison testing. It covers topics like what images are, different color spaces, advantages and disadvantages of image comparison, techniques for comparing two images like measuring color differences and using scale-invariant feature transform. Common problems with image comparison are also addressed, such as dealing with scale, as well as techniques like mean squared error and structural similarity to help resolve issues.
Automation Image Comparison Testing
- 1. Automating Image Comparison Testing Melvin Sandoval-2019
- 2. Agenda 1. What is an image? 2. Color spaces 3. Advantages / Disadvantages 4. Comparing two images 5. Problems on real world 6. Techniques to resolve them
- 3. What is an image? The computer represents an image as an matrix of pixels. For example:
- 4. Color spaces There are different color spaces, some of them are:
- 5. What is an image on RGB? If the image is on color the computer represents as a matrix of matrices
- 6. What is an image on RGB?
- 7. What is an image on RGB?
- 8. Advantages Disadvantages Faster UI verification against the human eyes Increase the test cases complexity Extend the capabilities of the automation process Handle the error ratio Desktop automation False positives/ False negatives
- 9. Comparing two images Comparing two images with the same size:
- 11. Images with the same size
- 12. Images with the same size
- 13. Images with the same size
- 18. Mean squared Error and Structural Similarity More examples: here
- 22. Questions