3-3

Question 1

histogram equalisation operation purpose

Answer 1

2nd method to enhance contrast of image (difference between max vs min value)

Question 2

PDF in Histogram Equalisation

Answer 2

Probability Density Function:
describes probability of certain pixel value found in image

Example:
if an image has total 100 pixels; 50 value of 0; black
20 value of 255; white
30 value of 175: grey
PDF for pixel value 0 is 0.5
PDF for pixel value 255 is 0.2
PDF for pixel value 175 is 0.3

Question 3

can u calculate histogram equalisation ???

Answer 3

y or n ???

Question 4

Histogram Equalisation on RGB by
Independent histogram equalization based on colour channels
Advantage, Disadvantage and Effect

Answer 4

Advantage: It processes fastest out of 3 methods

Disadvantage: Not considering the relevance of R, G and B channel but process then respectively will distort the image.

Effect: The intensities have been better distributed on the histogram but B’s color is out of balance

Question 5

Histogram Equalisation on RGB by
Histogram equalization based on average value of colour channels (3 effects)

Answer 5

Effect:
* RGB histogram is better distributed.
* Contrast increases.
* 3 histogram channels spread out but original relative distribution preserved

Question 6

Histogram Equalisation on RGB by
Intensity component equalization based on HSI* colour space
HSI, how, effect

Answer 6

HSI: Hue-Saturation-Intensity

how:
converted to HSI colour space,
then apply grayscale method to intensity
without resulting in changes to the hue & saturation of image

Effect:
Certain colours are too bright or dark which hides details
- caused by the uneven distribution of RGB histogram because equalization is on luminance channel of HSI color space