Effective lossy and lossless color image compression with Multilayer Perceptron

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    This paper presents the effective lossy and lossless color image compression algorithm with Multilayer perceptron. The parallel structure of neural network and the concept of image compression combined to yield a better reconstructed image with constant bit rate and less computation complexity. Original color image component has been divided into 8x8 blocks. The discrete cosine transform (DCT) applied on each block for lossy compression or discrete wavelet transform (DWT) applied for lossless image compression. The output coefficient values have been normalized by using mod function. These normalized vectors have been passed to Multilayer Perceptron (MLP). This proposed method implements the Back propagation neural network (BPNN) which is suitable for compression process with less convergence time. Performance of the proposed compression work is evaluated based on three ways. First one compared the performance of lossy and lossless compression with BPNN. Second one, evaluated based on different sized hidden layers and proved that increased neurons in hidden layer has been preserved the brightness of an image. Third, the evaluation based on three different types of activation function and the result shows that each function has its own merit. Proposed algorithm has been competed with existing JPEG color compression algorithm based on PSNR measurement. Resultant value denotes that the proposed method well performed to produce the better reconstructed image with PSNR value approximately increased by 21.62%.


  • Keywords

    Activation function; Back propagation neural network; Discrete cosine transform; Error; Hidden layer; JPEG compression

  • References

      [1] B. K. Patel, S. Agarwal, “Image compression techniques using artificial neural network”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 2, No. 10, (Oct 2013).

      [2] G. Qiu, T. J Terrel, M. R. Varely, “Improved image compression using back propagation networks”, Neural Network Applications and Tools - IEEE, DOI: 10.1109/NNAT. 1993. 586056, (1994), pp. 73 – 81.

      [3] K. Dilmililer,” Neural network implementation for medical image compression”, Journal of applied Mathematics. (2013) pp. 1 – 8.

      [4] N. Jaiwal, “Image compression using back propagation neural network”, International Journal of Science Engineering and Research, Vol. 3, No. 5, (2015), pp. 61 – 64.

      [5] N. Omaima, A. AL - Allaf, “Fast back propagation neural network algorithm for reducing convergence time of BPNN image compression”, Proceedings of the fifth International conference on IT & multimedia at ONITEN-IEEE, (Nov 2011),pp. 14 -16.

      [6] P. K. Charles, H. Khan, Ch. R. Kumar, N. Nikitha, S. Roy, V. Harish, M. Swathi, “Artificial neural network based image compression using Levenberg-Marquart algorithm”, International Journal of Modern Engineering Research, Vol. 1, No.2, pp. 482 - 489.

      [7] P. Sibi, S. A. Jones,P. Siddarth, “Analysis of different activation functions using back propagation neural networks”, Journal of theoretical and applied information technology, Vol. 47, No. 3, pp. 1264 – 1268.

      [8] R. A. Vasmatkar, S. P. Biradar, P. B. Shivashankar, “Artificial intelligence used for image compression”, Journal of BIOINFO Computational mathematics, Vol. 1, No. 1, (2011), pp. 05 – 10.

      [9] S. A. Ahamed, K. C. Shekarappa, “ANN implementation for image compression and decompression using back propagation techniques”, International Journal of Science and Research, Vol. 3, No. 6, (2014), pp. 1848 – 1851.

      [10] S. A. K. Jilani, S. A. Sattar, “JPEG Image compression using FPGA with artificial neural networks”, International Journal of Engineering and Technology, Vol. 2, No. 3, (2010), pp. 252 -257.

      [11] S. L. Pinjare, E. H. Kumar, “Implementation of Artificial neural network architecture for image compression using CSD multiplier”, Emerging Research in Computing, Information, Communication and Applications- Elsevier, (Aug 2013), pp. 581 – 587.

      [12] S. N. Sivanandam, S. Sumathi, S. N. Deepa, “Introduction to neural networks using matlab 6.0”, Tata McGraw Hill education private limited, (2009), pp. 187 - 188.

      [13] S. S. Panda, M. S. R. S. Prasad, MNM. Prasad, Ch. SKVR. Naidu, “Image compression using back propagation neural network”, International Journal of Engineering Science and advanced technology, Vol. 2, No. 1, pp. 74 -78.

      [14] V H. Gaidhane, V. Singh, Y. V. Hote, M. Kumar, “New approaches for image compression using neural network”, Journal of Intelligent Learning Systems and Applications, Vol. 3, (2011) pp. 220 – 229.

      [15] X. Liu, H. Gu, “Hyperbolic tangent function based 2 layers structure neural network”, International conference on Electronics and Optoelectronics- IEEE, (2011), pp. 376 – 379.

      [16] A. Bruna, “JPEG Advanced technology” available in file:///C:/phd%20study%20material/full%20JPEG%20(Bruna)%20working.pdf accessed on 22.09.2017

      [17] Images taken from “Computer vision test images” available at https://www.cs.cmu.edu/~cil/v-images.html accessed on 10.11.2017.

      [18] Standard images taken from” Image processing place” available at http://www.imageprocessingplace.com/root_files_V3/image_databases accessed on 10.11.2017.




Article ID: 11800
DOI: 10.14419/ijet.v7i2.22.11800

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.