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Frei-Chen bases based lossy digital image compression technique
پایگاه های Frei-Chen مبتنی بر تکنیک فشرده سازی تصویر دیجیتال از دست رفته-2020 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. Keywords: Big data problem | Compression factor | Frei-Chen basis | Run Length Encoding |
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