Module 1: Concept of Visual Information

Common values

There are standard values for the various parameters encountered in digital image processing. These values can be caused by video standards, by algorithmic requirements, or by the desire to keep digital circuitry simple. Table 1 gives some comm



Typical values







Gray Levels



Table 1: Common values of digital image parameters

Quite frequently we see cases of M=N=2k where .This can be motivated by digital circuitry or by the use of certain algorithms such as the (fast) Fourier transform.

The number of distinct gray levels is usually a power of 2, that is, where B is the number of bits in the binary representation of the brightness levels. When we speak of a gray-level image; when we speak of a binary image. In a binary image there are just two gray levels which can be referred to, for example, as "black" and "white" or "0" and "1".

Suppose that a continuous image is approximated by equally spaced samples arranged in the form of an array as:


Each element of the array refered to as "pixel" is a discrete quantity. The array represents a digital image.

The above digitization requires a decision to be made on a value for N a well as on the number of discrete gray levels allowed for each pixel.

It is common practice in digital image processing to let N=2n and G = number of gray levels = . It is assumed that discrete levels are equally spaced between 0 to L in the gray scale.

Therefore the number of bits required to store a digitized image of size is In other words a image with 256 gray levels (ie 8 bits/pixel) required a storage of bytes.

The representation given by equ (1) is an approximation to a continuous image.

Reasonable question to ask at this point is how many samples and gray levels are required for a good approximation? This brings up the question of resolution. The resolution (ie the degree of discernble detail) of an image is strangely dependent on both N and m. The more these parameters are increased, the closer the digitized array will approximate the original image.

Unfortunately this leads to large storage and consequently processing requirements increase rapidly as a function of N and large m.