lecture8

Image quantization
Image quantization is the process of reducing the image data by removing
some of the detail information by mapping group of data points to a
single point. This can be done by:
1. Gray Level reduction (reduce pixel values themselves I(r, c).
2. Spatial reduction (reduce the spatial coordinate (r, c).
The simplest method of gray-level reduction is Thresholding. We select a
threshold gray _level and set every thing above that value equal to “1”
and every thing below the threshold equal to “0”. This effectively turns a
gray level image into binary (two level) image and is often used as
preprocessing step in the extraction of object features, such as shape,
area, or perimeter.
A more versatile method of gray _level reduction is the process of taking
the data and reducing the number of bits per pixel. This can be done very
efficiency by masking the lower bits via an AND operation. Within this
method, the numbers of bits that are masked determine the number of
gray levels available.
Example:
We want to reduce 8_bit information containing 256 possible gray_level
values down to 32 possible values.
This can be done by ANDing each 8-bit value with the bit string
1111100.
this is equivalent to dividing by eight( 23 ), corresponding to the lower
three bits that we are masking and then shifting the result left three times.
[Gray _level in the image 0-7 are mapped to 0, gray_level in the range 8-
15 are mapped to 8 and so on].