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.
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
- Spatial Redundancy-: In spatial redundancy there is a correlation between the neighboring pixel values.
- Spectral Redundancy-: In spectral redundancy there is a correlation between different color planes or spectral bands.
- Temporal Redundancy-: In temporal redundancy there is a correlation between adjacent frames in the sequence of image.
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- Image Processing in Java | Set 3 (Colored image to greyscale image conversion)
- Image Processing in Java | Set 4 (Colored image to Negative image conversion)
- Image Processing in Java | Set 6 (Colored image to Sepia image conversion)
- Digital Image Processing Basics
- Difference between Opening and Closing in Digital Image Processing
- Digital Image Processing Chain
- Image Processing in Java | Set 5 (Colored to Red Green Blue Image Conversion)
- Image Processing in Java | Set 7 (Creating a random pixel image)
- Image Processing in Java | Set 8 (Creating mirror image)
- Image Processing in Java | Set 11 (Changing orientation of image)
- Image Processing in Java | Set 10 ( Watermarking an image )
- Getting started with Scikit-image: image processing in Python
- Image Processing in Java | Set 1 (Read and Write)
- Image Processing In Java | Set 2 (Get and set Pixels)
- Image Processing in Java | Set 9 ( Face Detection )
- Image Processing in Java | Set 12 ( Contrast Enhancement )
- Image Processing using OpenCV in Java | Set 13 (Brightness Enhancement)
- Image Processing using OpenCV in Java | Set 14 ( Sharpness Enhancement )
- Image Processing in Java | Set 14 ( Comparison of two images )
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