Redundancy in Digital Image Processing

Digital Image Processing means processing digital images by means of a digital computer. We can also say that it is the use of computer algorithms, in order to get enhanced images either to extract some useful information.

Note: For more information, refer to Digital Image Processing Basics

Redundancy in Image Processing

To understand Image redundancy or data redundancy in digital image processing lets look at the example. Let assume that 2 people Ramswarup and Suresh said a story. Suresh said the story in fewer words compare to Ramswarup where Ramswarup took too many words to said the same story. So either Ramswarup said un-relevant information/data which are not the part of the story or maybe he repeated his words more than once.

Redundancy refers to “storing extra information to represent a quantity of information”. So that is the redundancy of data now apply this concept on digital images we know that computer store the images in pixel values so sometimes image has duplicate pixel values or maybe if we remove some of the pixel values they don’t affect the information of an actual image. Data Redundancy is one of the fundamental component of Data Compression.

Data Compression

The Data Compression refers to the process of reducing the amount of data required to represent a given quantity of information. We know that one common characteristic followed by all the images is the neighboring of pixels and all the pixels are correlated to each other so there is a chance of existing redundant information.
Types of Redundancy in the context of neighboring pixels-: Broadly we have three types of redundancy in images pixels



  1. Spatial Redundancy-: In spatial redundancy there is a correlation between the neighboring pixel values.
  2. Spectral Redundancy-: In spectral redundancy there is a correlation between different color planes or spectral bands.
  3. Temporal Redundancy-: In temporal redundancy there is a correlation between adjacent frames in the sequence of image.

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