Image Compression (Lossy & Lossless Compressors) : Basic Idea and Rate of Compression (Especially for GATE-Geospatial 2022)

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To overcome the drawback of the large volume of information that a digital image needs to be stored, the images compression technique reduces the storage space.

One must be cautious about compressing images, especially in photogrammetry, as some compression algorithms generate irreparable losses of information that reduce the quality of the image.

Compressing an image involves reducing the amount of unnecessary data to represent the digital image. This technique is based on the elimination of all redundant data in the image. The more redundancy in the image, the more compression it can suffer.

The image shown in the figure has 1 bit (B/W) :

Example of a B/W Image to Compress

Example of a b/w image to compress

The first row of the image would have the following values:

WWWWWWWWWBWWWWWWWWWWWWBBBWWWWWWWWW WWWBWWWWWWWWW

Without compressing it, it would need a memory of 47 bits just to store this row.

However, it would be possible to reduce the number of bits by expressing it in the following manner:

9W1B12W3B12W1B19W

In this way, only 17 bits would be needed to store the same row.

Redundancy has been eliminated. This redundancy consisted of the number of times that the same value was repeated in neighbour pixels in the same row.

Rate of Compression

A concept that needs to be mentioned is the rate of compression, which refers to the relation that exists between the original image and the compressed one. For instance, a 1.5: 1 rate of the compression means that the original image occupies 1.5 more space than the compressed one.

Lossy & Lossless Compressors

  • The greatest difference that exists between compression algorithms is that some of them lose information to reduce even more the size of the file. These are the so-called Lossy Compressors. When decompressing a compressed image to display it in a screen, these algorithms are not able to reproduce it exactly as if it were the original image; thus, they suffer a loss of information. This loss is minimum and the human eye cannot perceive it. In the cases when the metric of the image is its main utilisation, it is impossible to use this type of algorithms.
  • In the case of photogrammetry, the only algorithms that should be used are lossless compressors, so that even if they do not really reduce the size of the images, they conserve their integrity, which is essential to conserve, in addition, their metric attributes.

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