A Deep Wavelet AutoEncoder Scheme for Image Compression


  • Houda Chakib Data4Earth Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane, B.P 523, Beni Mellal 23000, Morroco
  • Najlae Idrissi Data4Earth Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane, B.P 523, Beni Mellal 23000, Morroco


Wavelet Transform DWT, Unsupervised Neural Network, AutoEncoder, Approximate Image, RGB Image, Image Compression


For many years and since its appearance, Digital Wavelet Transform DWT has been used with great success in a wide range of applications especially in image compression and signal de-noising. Combined with several and various approaches, this powerful mathematical tool has shown its strength to compress images with high compression ratio and good visual quality. This paper attempts to demonstrate that it is needless to follow the classical three stages process of compression: pixels transformation, quantization and binary coding when compressing images using the baseline method. Indeed, in this work, we propose a new scheme of image compression system based on an unsupervised convolutional neural network AutoEncoder (CAE) that will reconstruct the approximate sub-band issue from image decomposition by the wavelet transform DWT. In order To evaluate the model’s performance we use Kodak dataset containing a set of 24 images never compressed with a lossy algorithm technique and applied the approach on every one of them. We compared our achieved results with those obtained using standard compression method. We draw this comparison in terms of four performance parameters: Structural Similarity Index Metrix SSIM, Peak Signal to Noise Ratio PSNR, Mean Square Error MSE and Compression Ratio CR. The proposed scheme offers significate improvement in distortion metrics over the traditional image compression method when evaluated for perceptual quality moreover it produces better visual quality images with clearer details and textures which demonstrates its effectiveness and its robustness.


L. Xiang and J. Shihaho, ? Neural Image Compression and Explanation,? arXiv: 1908.08988v2 [cs.CV] December 2020.

K. He, X. Zhang, S. Ren and J. Sun, ? Deep Residual Learning for Image Recognition,? IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770-778, 2016.

J. Devlin, M.-W.Chang, K, Lee and K. Toutanova, ? Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding,? arXiv preprint arXiv: 1810.04805, 2018.

E. Battenberg, J. Chen, R. Child, A. Coates, Y. Gaur, Y. Li, H. Liu and al. ?Exploring Neural Transducers for End-To-End Speech Recognition,? arXiv preprint arXiv: 1707.07413, 2017.

M. Alam, M.D. Samad, L. Vidyaratne, A. Glandon and K.M. Iftekharuddin, ? Survey on Deep Neural Networks in Speech and Vision Systems,? Neurocomputing 417 , 302–321, 2020.

G. De-Las-Heras, J. Sánchez-Soriano and E. Puertas, ? Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads,? Sensors, 21, 5866, 2021.

A. Krizhevsky, I. Sutskever, and G. Hinton, ? ImageNet Classification with Deep Convolutional Neural Networks, ? in NIPS, 2012.

A.M. Kramer, ? Nonlinear Principal Component Analysis using Autoassociative Neural Networks, ?. AIChE Journal. 37 (2): 233–243, 1991.

P. Vincent, H. Larochelle, I, Lajoie, Y. Bengio and P-A. Manzag, " Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, " Journal of Machine Learning Research. 11: 3371–3408, 2010.

G.E, Hinton, A. Krizhevsky A and S.D. Wang, ? Transforming auto-encoders, " In International Conference on Artificial Neural Networks, 14 (pp. 44-51). Springer, Berlin, Heidelberg, Jun 2011.

A. Géron, ? Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, ? Canada: O’Reilly Media, Inc. pp. 739–740, 2019.

L. Cheng-Yuan, H. Jau-Chi and Y. Wen-Chie," Modeling word perception using the Elman network," Neurocomputing. 71 (16–18): 3150, 2008.

L. Cheng-Yuan, C. Wei-Chen, L. Jiun-Wei and L. Daw-Ran, "Autoencoder for words," Neurocomputing. 139: 84–96, 2014.

P. Diederik, Welling, Max and Kingma,. "An Introduction to Variational Autoencoders ," Foundations and Trends in Machine Learning. 12 (4): 307–392. arXiv:1906.02691, 2019.

S.G. Mallat, ?A theory for multi resolution signal decomposition: The wavelet representation,? IEEE Trans. Pattern Anal. Machine Intell, vol. 11, pp. 674-693, 1989.

M. Rabbani and P.W. Jones, ? Digital Image Compression Techniques,?vol.TT07, SPIE Press Book, Bellingham, Washington, USA, Feb 1991

M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies, ? Image coding using wavelet transform, ? IEEE Trans. Image Process. 1 (2), pp. 205-220, 1992.

A. Said and W.A. Pearlman, ?A new fast and efficient image codec based on set partitioning in hierarchical trees, ? IEEE, Trans. Circuits syst. Video Technol. 6 (3), pp. 243-250, 1996.

Q. Zang, ? Wavelet Network in Nonparametric Estimation, ? IEEE Trans. Neural Networks, 8(2), pp. 227-236, 1997.

K. Ratakondu, and N. Ahuja, ? Loless Image Compression with Multiscale Segmentation, ? IEEE, Transactions on Image Processing, vol. 11, No. 11, pp. 1228-1237, 2002.

G.K. Wallace, ? The JPEG still picture compression standard,? IEEE Trans. On Consumer Electron., vol. 38 (1), pp. xviii-xxxiv, Feb. 1992.

W.B. Pennebaker and J. L. Mitcell, ?JPEG: Still Image Data Compression Standard,? New York: Van Nostrand Reinhold, 1993.

M. Rabbani and R. Joshi, "An Overview of the JPEG2000 Still Image Compression Standard," Signal Processing: Image Communication Journal, Volume 17, Number 1, October 2001.

A. Skodras, C. Charilaos and T. Ebrahimi, ? The JPEG 2000 Still Image Compression Standard, ? IEEE Signal Processing magazine. September 2001.

D. Taubman and P. Marcellin, ?JPEG2000: Image Compression Fundamentals, Practice and Standards,? Kluwer Academic Publishers, 2001.

D. Taubman, and M. Marcellin, ?JPEG2000 Image Compression Fundamentals, Standards and Practice: Image Compression Fundamentals, Standards and Practice,? Springer Science & Business Media. ISBN 9781461507994, 2012.

J. Liang and R. Talluri, ?Tools for robust image and video coding in JPEG 2000 and MPEG-4 Standards,? in Proc. SPIE Visual Communications and Image Processing Conf. (VCIP), San Jose, CA, Janvier 1999.

M.W. Marcellin, M. Gormish, A. Bilgin, and M. Boliek, ?An overview of JPEG 2000,? in Proc. IEEE Data Compression Conf., Snowbird, UT, pp. 523-541, Mar. 2000,

D. Santa Cruz and T. Ebrahimi, ?An analytical study of the JPEG 2000 functionalities,? in Proc. IEEE Int. Conf. Image Processing (ICIP 2000), Vancouver, Canada, 10-13, vol. II, pp. 49-52, September. 2000.

D. Santa Cruz, M. Larsson, J. Askelof, T. Ebrahimi, and C. Christopoulos, ?Region of interest coding in JPEG 2000 for interactive client/server applications,? in Proc. IEEE Int. Workshop Multimedia Signal Processing, Copenhagen, Denmark, p. 389-384, September 1999.

A.T. Kouanou, D. Tchiotsop, R. Tchinda and Z.D. Tansaa, ?A Machine Learning Algorithm for Image Compression with application to Big Data. Architecture: A Comparative Study,? British Biomedical Bulletin. British Biomedical Bulletin. Vol.7 No. 1:316, 2019.

Q. Zang, and A. Beneveniste, ?Wavelet networks,? IEEE Tans. Neural Networks, 7(1), pp. 889-898, 1992.

S. Osowski, R. Waszczuk, and P. Bojarczak, ?Image Compression Using Feed Forward Neural Networks- Hierarchical Approach,? Lecture Notes in Computer Science, Book chapter, Springer – Verlag, 3497, pp. 1009-1015, 2006.

Q. Zang, ?Wavelet Network in Nonparametric Estimation,? IEEE Trans. Neural Networks, 8(2), pp. 227-236, 1997.

A.V. Singh, and K.S. Murthy, “Neuro-Wavelet based Efficient Image Compression Using Vector Quantization,” International Journal of Computer Applications (0975-08887), vol. 49-N°.3, July 2012.

K. Ahmadi, A.Y. Javaid, and E. Salari, “An efficient Compression Scheme based on Adaptive Thresholding in Wavelet Domain using Particle Swarm Optimization,” signal processing: image communication 32, 2015, pp.33-39.

V. Krishnanaik, G.M. Someswar, K. Purushotham and A. Rajaiah, “Implementation of Wavelet Transform, DPCM and Neural Network for Image Compression,” International Journal of Engineering and Computer science ISSN: 2319-7242, vol. 2, issue. 8, pp. 2468-2475, August 2013.

T. Denk, K. Perhi, and V. Cherkassky, “Combining Neural Network and the Wavelet Transform for Image Compression,” Proceeding of Intl Conf, pp. 637-640, 1993.

K. Dimililer and A. Khashman, “Image Compression using Neural Networks and Haar wavelet,” Transaction on Signal Processing, ISSN: 1790-5052, vol. 4, issue: 5, May 2008.

A.K. Alexandridis and A.D. Zaprani, “Wavelet Neural Networks: A Pratical Guide,” neural networks 42, pp. 1-27, 2013.

L. Theis and M. Bethge, ? Generative Image Modeling Using Spatial LSTMs,? Advances in Neural Information Processing Systems 28, 2015.

L. Theis, A. van den Oord, and M. Bethge, ? A Note on the Evaluation of Generative Models, ? International Conference on Learning Representations, 2016.

G. Toderici, S. M. O’Malley, S. J. Hwang, D. Vincent, D. Minnen, S. Baluja, M. Covell, and R. Sukthankar, ?Variable rate image compression with recurrent neural networks,? International Conference on Learning Representations, 2016a.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, ? Full Resolution Image Compression with Recurrent Neural Networks, ?. Computer Vision and Pattern Recognition, arXiv:1608.05148v1, 2016b.

J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational image compression with a scale hyperprior,” in Proc. of International Conference on Learning Representations, 2018.

M. Tschannen, E. Agustsson, and M. Lucic, “Deep generative models for distribution-preserving lossy compression,” in Proc. of Advances in Neural Information Processing Systems, 2018.

F. Yang , L. Herranz, J. van de Weijer, J. A. Iglesias Guitian , A. M. Lopez and G. Mozerov, ?Variable Rate Deep Image Compression With Modulated Autoencoder,? IEEE Signal Processing Letters, Vol. 27, 2020.

Y. Chuxi, Y. Zhao and S. Wang, ? Deep Image Compression in the Wavelet Transform Domain Based on High Frequency Sub-Band Prediction,? IEEE Computer Science. April 2019.

H. Ma, D. Liu, N. Yan, H. Li and F. Wu, ?End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform,? IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 44 issue: 3- 2020.

T. Williams and R. Li, ? An Ensemble of Convolutional Neural Networks Using Wavelets for Image Classification,? Journal of Software Engineering and Applications. Vol.11 No.02, 2018.

A. Paul, A. Kundu, N. Chaki and al, ? Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising,? Multimed Tools Appl 81, 2529–2555, 2022.

Q. Feng, Q. Yin and P. Guo, ?Image Recognition With Haar Wavelet and Pseudoinverse Learning Algorithm Based Autoencoders,? Journal of Physics: Conference Series, Volume 2278, 2022 6th International Conference on Machine Vision and Information Technology (CMVIT 2022) Feb. 25, 2022 Online.

Q. Zhu, H. Wang and R. Zhang, "Wavelet Loss Function for Auto-Encoder," in IEEE Access, vol. 9, pp. 27101-27108, 2021.

H. Luo, Y. Yan Tang, R.P. Biuk-Aghai, X. Yang, L. Yang and Y. Wang, ?Wavelet-based extended morphological profile and deep autoencoder for hyperspectral image classification,? International Journal of Wavelets, Multiresolution and Information Processing. Vol. 16, No. 03, 1850016, 2018.

Q. Huynh-Thu and M. Ghanbari, M, "Scope of validity of PSNR in image/video quality assessment". Electronics Letters. 44 (13): 800. 2008.

Q. Huynh-Thu and M. Ghanbari, "The accuracy of PSNR in predicting video quality for different video scenes and frame rates". Telecommunication Systems. 49 (1): 35–48. Janv 2012.

Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, ?Image Quality Assessment: from error Visibility to Structural Similarity,?. IEEE Trans Image Process;13:600-12, 2004.

Z. Wang, E.P. Simoncelli and A.C. Bovik, ?Multiscale structural similarity for image quality assessment,? Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2. pp. 1398–1402, 2004.

R. Rozema, H.T. Kruitbosch, B. van Minnen, B. Dorgelo, J. Kraeima and PMA Van Ooijen, ? Structural Similarity Analysis of Midfacial Fractures—a feasibility study, ? Quant Imaging Med Surg, 12(2): 1571–1578, Feb 2022.

G.P. Renieblas, A.T. Nogués, A.M. González, N. Gómez-Leon and E.G. Del Castillo, ?Structural Similarity Index Family for Image Quality Assessment in Radiological Images, ? J Med Imaging (Bellingham);4:035501, 2017.

P. Parsania1 and P. Virparia, ?A Review: Image Interpolation Techniques for Image Scaling, ? International Journal of Innovative Research in Computer and Communication Engineering. Vol. 2, Issue 12, December 2014.

F. Farnoush, ? Learning Activation Functions in Deep Neural Networks, ? Ecole Polytechnique, Montreal (Canada)?ProQuest Dissertations Publishing, 10957109, 2017.

C.E. Nwankpa, W. Ijomah, A. Gachagan and S. Marshall, ? Activation Functions: Comparison of Trends in Practice and Research for Deep Learning,? arXiv:1811.03378v1 [cs.LG] 8 Nov 2018.

P.D. Kingma and J. Lei Ba, ADAM: ?A Method For Stochastic Optimization,? arXiv:1412.6980v9 [cs.LG] 30 Jan 2017.

S. Ioffe and C. Szegedy, ? Batch normalization: Accelerating deep network training by reducing internal covariate shift,? Machine Learning, arXiv:1502.03167[cs.LG], 2015.




How to Cite

Chakib, H., & Idrissi, N. (2023). A Deep Wavelet AutoEncoder Scheme for Image Compression. American Scientific Research Journal for Engineering, Technology, and Sciences, 91(1), 87–104. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/8553