Solution:

Image compression is possible because images in general are highly coherent, meaning there is redundant information.

Compression is achieved through redundancy and irrelevancy reduction.

Redundancy means duplication and irrelevancy means the part of the image information that the human visual system will not notice.

Redundancy can be broadly classified into,

(1) Statistical Redundancy.

(2) Coding Redundancy.

Statistical redundancy can be further classified into,

(1) Interpixel redundancy.

(2) Coding redundancy.

(1) Inter pixel Redundancy: Inter-pixel redundancy is due to the correlation between neighboring pixels It means that the neighboring pixels are not statistically independent.

The interpixel correlation is referred to as inter-pixel redundancy.

Image files frequently contain the same character repeated many times in a row.

Each row of such images can have long runs of the same gray value.

In such cases, one can store a code specifying the value of the gray level, followed by the length of the run, rather than storing the same value many times over.

For example,

The first code specifies the gray value followed by the length of the run.

Hence RLE of this row is,

2 3 5 8 6 4

Run length encoding achieves considerable compaction in images that have a fairly The RLE eliminates interpixel redundancies.

(2) Coding Redundancy:-

Coding redundancy is associated with the representation of information. The information is represented in the form of code.

we can develop a code such that fewer bits are assigned to gray levels having a higher probability of occurrence and vice versa we could reduce the number of bits required for transmission.

Such coding is known as variable length coding or Entropy coding and it eliminates.

The Huffman code and arithmetic code are examples of codes.