Next Article in Journal
Some General Classes of q-Starlike Functions Associated with the Janowski Functions
Previous Article in Journal
Toward Self-Driving Bicycles Using State-of-the-Art Deep Reinforcement Learning Algorithms
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

1
Computer Engineering Department, College of Engineering, Mustansiriyah University, Baghdad 10047, Iraq
2
Civil Engineering Department, University of Technology, Baghdad 10066, Iraq
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(2), 291; https://doi.org/10.3390/sym11020291
Received: 3 December 2018 / Revised: 6 February 2019 / Accepted: 21 February 2019 / Published: 23 February 2019
  |  
PDF [10884 KB, uploaded 23 February 2019]
  |  

Abstract

Image compression is one of the most interesting fields of image processing that is used to reduce image size. 2D curve-fitting is a method that converts the image data (pixel values) to a set of mathematical equations that are used to represent the image. These equations have a fixed form with a few coefficients estimated from the image which has been divided into several blocks. Since the number of coefficients is lower than the original block pixel size, it can be used as a tool for image compression. In this paper, a new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients. The main disadvantages of previous approaches were the additional errors and degradation of edges of the reconstructed image, as well as the blocking effect. To overcome this deficiency, it is proposed that this symmetric hyperbolic tangent (tanh) function be used instead of the classical 1st- and 2nd-order curve-fitting functions which are asymmetric for reformulating the blocks of the image. Depending on the symmetric property of hyperbolic tangent function, this will reduce the reconstruction error and improve fine details and texture of the reconstructed image. The results of this work have been tested and compared with 1st-order curve-fitting, and standard image compression (JPEG) methods. The main advantages of the proposed approach are: strengthening the edges of the image, removing the blocking effect, improving the Structural SIMilarity (SSIM) index, and increasing the Peak Signal-to-Noise Ratio (PSNR) up to 20 dB. Simulation results show that the proposed method has a significant improvement on the objective and subjective quality of the reconstructed image. View Full-Text
Keywords: image compression; hyperbolic function; curve-fitting; blocking effect; Structural SIMilarity (SSIM) index image compression; hyperbolic function; curve-fitting; blocking effect; Structural SIMilarity (SSIM) index
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Khalaf, W.; Zaghar, D.; Hashim, N. Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function. Symmetry 2019, 11, 291.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top