Abstract
In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.
Highlights
In recent decades, many algorithms have been proposed in the field of digital data compression [1,2], all of which have focused on reducing the redundancy of digital data to© Mahmood Al-khassaweneh and Omar AlShorman
In order to make use of this repetition and redundancy, we propose to use the well-known Run-Length Encoding (RLE) algorithm to further compress the image
The results show that the first stage of the proposed algorithm produce highly correlated decompressed image with small mean square error (MSE)
Summary
Many algorithms have been proposed in the field of digital data compression [1,2], all of which have focused on reducing the redundancy of digital data to. The full terms of this license may be seen at http://creativecommons. The full terms of this license may be seen at http://creativecommons. org/licences/by/4.0/legalcode
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