Image processing aims to reproduce a real-world scene at the same perceptual level as the original, not to judge artistic quality. Quality assessment measures how much an image has changed from its source state.
Sources of Change
Artefacts
Unwanted details from side-effects of capture, quantization and processing. Known ahead of time in most instances. Easy to estimate and quantify.
Noise
Unwanted details from internal or external sources, mostly from analog stages. Random in most instances, though it may follow a known probability model. Difficult to quantize and estimate.
Objective Assessment
Based on mathematical formulae comparing an image to a reference source. Independent of human judgment. May not align with artistic or perceptual judgment
Here:
- : image dimensions
- : pixel value in the processed image
- : pixel value in the source image
A higher MSE means more change between the images, but this isn’t necessarily aligned with subjective quality. A restoration process, for instance, deliberately changes pixel values to improve perceived quality.
Subjective Assessment
Based on human perception, expressed as a textual description of image quality.
- Different observers may disagree. The same observer may disagree with themselves at different times.
- Requires multiple assessments, from multiple subjects and images, to avoid bias
- Needs a normalized rating scale
Goodness scale:
| Value | Description |
|---|---|
| 5 | Excellent |
| 4 | Good |
| 3 | Fair |
| 2 | Poor |
| 1 | Unsatisfactory |
Impairment scale:
| Value | Description |
|---|---|
| 5 | Extremely objectionable |
| 4 | Definitely objectionable |
| 3 | Impairment, but not objectionable |
| 2 | Just noticeable |
| 1 | Not noticeable |