# Bit-plane Slicing

You probably know that everything on a computer is stored as strings of bits. In Bit-plane slicing, we take the advantage of this fact to perform various image operations. Let’s see how.

I hope you have basic understanding of binary and decimal relationship.

For an 8-bit image, a pixel value of 0 is represented as 00000000 in binary form and 255 is encoded as 11111111. Here, the leftmost bit is known as the most significant bit (MSB) as it contributes the maximum. e.g. if MSB of 11111111 is changed to 0 (i.e. 01111111), then the value changes from 255 to 127. Similarly, rightmost bit is known as Least significant bit (LSB).

In Bit-plane slicing, we divide the image into bit planes. This is done by first converting the pixel values in the binary form and then dividing it into bit planes. Let’s see by an example.

For simplicity let’s take a 3×3, 3-bit image as shown below. We know that the pixel values for 3-bit can take values between 0 to 7.

I hope you understand what is bit plane slicing and how it is preformed. Next Question that comes to mind is What’s the benefit of doing this?

#### Pros:

• Image Compression (We will see later how we can construct nearly the original image using less number of bits).
• Converting a gray level image to a binary image. In general, images reconstructed from bit planes is similar to applying some intensity transformation function to the original image. e.g. Image reconstructed from MSB is same as applying thresholding function to the original image. We will validate this in the code below.
• Through this, we can analyze the relative importance of each bit in the image that will help in determining the number of bits used to quantize the image.

Let’s see how we can do this using OpenCV-Python

#### Code

The output looks like this 8 bit planes (Top row – 8,7,6,5 ; bottom – 4,3,2,1 bit planes)

Clearly from the above figure, the last 4 bit planes do not seem to have much information in them.

Now, if we combine the 8,7,6,5 bit planes, we will get approximately the original image as shown below.