Image Quality Assessment | Sahithyan's Notes

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.

Always gives the same result, regardless of who performs it
Can be automated
May not align with artistic or perceptual judgment

MSE = \frac{1}{MN}\sum_{x=1}^{M}\sum_{y=1}^{N}(p(x,y) - r(x,y))^2

Here:

$M, N$ : image dimensions
$p(x,y)$ : pixel value in the processed image
$r(x,y)$ : pixel value in the source image

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:

5: Excellent
4: Good
3: Fair
2: Poor
1: Unsatisfactory

Impairment scale:

5: Extremely objectionable
4: Definitely objectionable
3: Impairment, but not objectionable
2: Just noticeable
1: Not noticeable