Image quantization

2.6 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 a binary (two-level) image and is often used as a 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 11111000 this is equivalent to dividing by eight( ( , 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].

We can see that by masking the lower three bits we reduce 256 gray levels to 32 gray levels:
256 ÷ 8= 32