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Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

Mohammed Omari1,*, Souleymane Ouled Jaafri1

LDDI Laboratory, Mathematics and Computer Science Department, University of Adrar, Adrar 01000, Algeria.

* Corresponding Author: Mohammed Omari. Email: email.

Computers, Materials & Continua 2019, 59(3), 753-775.


Image compression is a process based on reducing the redundancy of the image to be stored or transmitted in an efficient form. In this work, a new idea is proposed, where we take advantage of the redundancy that appears in a group of images to be all compressed together, instead of compressing each image by itself. In our proposed technique, a classification process is applied, where the set of the input images are classified into groups based on existing technique like L1 and L2 norms, color histograms. All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook. In the process of extracting the different sub-images, we used the mean squared error for comparison and three blurring methods (simple, middle and majority blurring) to increase the compression ratio. Experiments show that varying blurring values, as well as MSE thresholds, enhanced the compression results in a group of images compared to JPEG and PNG compressors.


Cite This Article

M. Omari and S. Ouled Jaafri, "Application of image compression to multiple-shot pictures using similarity norms with three level blurring," Computers, Materials & Continua, vol. 59, no.3, pp. 753–775, 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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