The criteria for an assessment of a quality of an image are (i) Objective Fidelity Criteria and (ii) Subjective Fidelity Criteria.

1. Objective Fidelity Criteria:

   • Mean Square Error (MSE): Let f(x,y) represent an input image and let $\hat f(x,y)$ denote an estimate or approximate of f(x,y) for any value of x and y, The mean squared error is defined as, $$MSE = \frac{1}{MxN} \sum_{x=0}^{M-1} /sum_{y=0}^{N-1} [f(x,y) - \hat f(x,y)]^2$$
   • Signal to Noise Ratio (SNR): $$SNR = \frac{\sum_{x=0}^{M-1} \sum_{y=0}^{N-1} [f(x,y)]^2}{\sum_{x=0}^{M-1} \sum_{y=0}^{N-1} [f(x,y) - \hat f(x,y)]^2}$$

2. Subjective Fidelity Criteria:

• Images are viewed by human beings. Therefore measuring image quality by the subjective evaluations of a human observer is more appropriate. This can be
  accomplished by showing a typical decompressed image to an appropriate cross section of viewers and averaging their evaluations.
• The evaluations may be made by using an absolute rating scale or by means of side by side comparison of f(x,y)and f ̂(x,y).
• Side by side comparisons can be done with the following scale: - {1, 2, 3, 4, 5, 6}to represent evaluations such as {Excellent, Fine, Passable, Marginal, Inferior, Unusable} respectively. - {-3, -2, -1, 0, 1, 2, 3} to represent subjective evaluations such as {much worse, worse, slightly worse, the same, slightly better, better, much better} respectively.

These evaluations are said to be based on subjective fidelity criteria: