TY  - EJOU
AU  - Omari, Mohammed  
AU  - Jaafri, Souleymane Ouled  

TI  - Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 59
IS  - 3
SN  - 1546-2226

AB  - 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 L<sub>1</sub> and L<sub>2</sub> 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.
KW  - Image compression
KW  -  simple blurring
KW  -  middle blurring
KW  -  majority blurring
KW  -  similarity
KW  -  classification
KW  -  mean squared error

DO  - 10.32604/cmc.2019.06576