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Pre and Postprocessing for JPEG to Handle Large Monochrome Images

by Walaa Khalaf *,†, Abeer Al Gburi and Dhafer Zaghar
Computer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, Iraq
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Algorithms 2019, 12(12), 255; https://doi.org/10.3390/a12120255
Received: 12 October 2019 / Revised: 25 November 2019 / Accepted: 26 November 2019 / Published: 1 December 2019
Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images. View Full-Text
Keywords: image compression; JPEG; hyperbolic function; PSNR; SSIM; compression ratio; optimization image compression; JPEG; hyperbolic function; PSNR; SSIM; compression ratio; optimization
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Khalaf, W.; Al Gburi, A.; Zaghar, D. Pre and Postprocessing for JPEG to Handle Large Monochrome Images. Algorithms 2019, 12, 255.

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