Content-Based Image Compression Using Hybrid Discrete Wavelet Transform with Block Vector Quantization
Keywords:
BVQ, Block Variance, Quantization, Compression ratioAbstract
Image compression is necessary for the conveyance of information in the form of images. Images that have been compressed are fewer in size and sent over networks more quickly. Many algorithms focus on compressing images without prior knowledge on the image content type. But certain applications require content-based compression where degree of compression is controlled based on the image content type and should be able recover completely without loss of information. The proposed work aims at compressing the images based on the contrast variations and hybridizing discrete wavelet transform (DWT) and block vector quantization (BVQ) techniques. Two level DWT is applied on the image, then each sub-band is divided into non-overlapping blocks and a decision is made for each block based on the block variance before going for quantization. The proposed work calculates variance at the local regions to make decision as lower and higher contrast blocks, this helps to control degree of compression as only redundant/repeated blocks are allowed for quantization by preserving the edge information. Considering the entire image at once for vector quantization (VQ) diminishes the images' quality of compression. The VQ compression method often makes use of codebooks which possess lack of optimization. The proposed work implements BVQ technique, where only minimal pixels in a block are considered for quantization at once. This technique greatly reduces the computation time and also increases compression ratio. At last, huffman encoding is applied to the quantized coefficients. Following that, the bits that constitute the compressed image are saved and later restored. The suggested approach compresses and reconstructs images with adequate quality, on number of standard images as implemented. The effectiveness of the suggested work is also assessed by testing with custom real time images. When compared to current approaches, the findings suggest that the proposed work outperforms.
Downloads
References
Mohammed F. Radad, Ali O. Al-Shimmery & Ali H. Nasir. (2022). “A Hybrid Discrete Wavelet Transform with Vector Quantization for Efficient Medical Image Compression”, NeuroQuantology|July2022|Volume20|Issue8|Page 8868-8876|DOI:10.14704/nq.2022.20.8. NQ44909.
Javad Rahebi. (2022). “Vector quantization using whale optimization algorithm for digital image compression”, Multimedia Tools and Applications 81:20077–20103. https://DOI.org/10.1007/s11042-022-11952-x
Bilal M, Ullah Z, Islam IU, Ihtesham ul islam. (2021). “Fast codebook generation using pattern-based masking algorithm for image compression”. IEEE Access 9:98904–98915.2021.
Shuying Xu, Chin-Chen Chang & Yanjun Liu (2021) “A novel image compression technology based on vector quantisation and linear regression prediction”, Connection Science, 33:2, 219-236, DOI: 10.1080/09540091.2020.1806206.
U. Naveenkumara and A. Padmannabha reddy. (2021). “An optimized BTC image compression technique based in singular value thresholding in the wavelet domain”, Advances and Applications in Mathematical Sciences Volume 20, Issue 9, July 2021, Pages 2031-2044 © 2021 Mili Publications.
Gaurav Kumar and Rajeev Kumar. (2021). “Analysis of Arithmetic and Huffman Compression Techniques by Using DWT-DCT”, I.J. Image, Graphics and Signal Processing, 2021, 4, 63-70, DOI: 10.5815/ijigsp.2021.04.05.
Taiwo Samuel Aina, Oluwaseun Olanrewaju Akinte, Babatunde Ademola, Iyaomolere, Innocent Iriaghuan Abode. (2021). “Wavelet Transforms and Image Approximation Based Image Compression System”, International journal of scientific & technology research Vol 10, Issue 10, October 2021 pp 104-107.
S. Boopathiraja and P. Kalavathi. (2021). “A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet Transform”, Int. J. Biomedical Engineering and Technology, Vol. 35, No. 3, 2021 pp no 191-206.
Rajaa Khalaf Gaber, Ahmed Abdulqader Hussein, Manal Kadhim Oudah and Ahmed Hameed Reja. (2020). “Image Compression Using High Level Wavelet Transformer with Non-Uniform Quantizer and Different Levels Huffman Codes”. IOP Conf. Series: Materials Science and Engineering 765 (2020) 012072. DOI:10.1088/1757-899X/765/1/012072.
Saradha Rani Sabbavarapu, Sasibhushans Rao Gottapu and Prabhakara Rao Bhima. (2020). “A discrete wavelet transforms and recurrent neural network based medical image compression for MRI and CT images”, Journal of Ambient Intelligence and Humanized Computing, Springe June 2020. https://DOI.org/10.1007/s12652-020-02212-7.
Chavan PP, Rani BS, Murugan M, Chavan P. (2020). “A novel image compression model by adaptive vector quantization: modified rider optimization algorithm”. Sadhana (2020) 45(1):1–15. https://DOI.org/10.1007/s12046-020-01436-9T.
Paul Nii Tackie Ammah and Ebenezer Owusu. (2019). “Robust medical image compression based on wavelet transform and vector quantization”, Informatics in Medicine Unlocked, vol. 15, no. April, p. 100183, 2019, DOI: 10.1016/j.imu.2019.100183.
Marcos Roberto e Souza, Anderson Carlos Sousa e Santos, and Helio Pedrini. (2020). “A Hybrid Approach Using the k-means and Genetic Algorithms for Image Color Quantization”, Recent Advances in Hybrid Metaheuristics for Data Clustering, First Edition. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd. Pp no 151-171.
K. Chiranjeevi and U. R. Jena. (2018). “Image compression based on vector quantization using cuckoo search optimization technique”, Ain Shams Eng. J., vol. 9, no. 4, pp. 1417-1431, Dec. 2018.
Ms. D. Preethi and Dr. D. Loganathan. (2020). “Time Complexity Analysis of Optimal Firefly Vector Quantization Algorithms for Image Compression”, Proceedings of the Fifth International Conference on Inventive Computation Technologies (ICICT-2020) IEEE Xplore Part Number: CFP20F70-ART.
Srijati Agrawal. (2020). “Finite-State Vector Quantization Techniques for Image Compression”, International Research Journal of Innovations in Engineering and Technology (IRJIET) ISSN (online): 2581-3048 Volume 4, Issue 7, pp 1-8, July-2020 https://DOI.org/10.47001/IRJIET/2020.407001.
H. B. Kekre, Prachi Natu, and Tanuja Sarode. (2016). “Color Image Compression using Vector Quantization and Hybrid Wavelet Transform”, Twelfth International Multi-Conference on Information Processing-2016 (IMCIP-2016) Published by Elsevier. DOI: 10.1016/j.procs.2016.06.059.
Wen-Jan Chen, Wen-Tsung Huang. (2009). “VQ indexes compression and information hiding using hybrid lossless index coding”, Digital Signal Processing 19 (2009) 433–443. DOI: 10.1016/j.dsp.2008.11.003.
Owais Rashid, Asdaq Amin and Mohd Rafi Lone. (2020). “Performance Analysis of DWT Families”, Proceedings of the Third International Conference on Intelligent Sustainable Systems [ICISS 2020]. IEEE Xplore.
Shaimaa Othman, Amr Mohamed, Abdelatief Abouali and Zaki Nossair. (2020). “Performance Improvement of Lossy Image Compression Based on Polynomial Curve Fitting and Vector Quantization”, Information and Communication Technology for Competitive Strategies (ICTCS 2020), Springer Nature Singapore Pte Ltd. 2021. https://DOI.org/10.1007/978-981-16-0882-725.
Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja and Huiling Chen. (2019). “Harris hawks’ optimization: Algorithm and applications”, Future Generation Computer Systems vol 97 (2019) pp no 849–872.
Jui-Sheng Chou, Dinh-Nhat Truong. (2020). “A novel metaheuristic optimizer inspired by bahavior of jellyfish in ocean”, Applied Mathematics and Computation 389 (2020) 125535. https://DOI.org/10.1016/j.amc.2020.125535
Chiranjeevi Karri and Umaranjan Jena. (2016). “Fast vector quantization using a bat algorithm for image compression”, Eng. Sci. Technol., In t. J., vol. 19, pp. 769_781, Jun. 2016.
DOI: 10.1016/j.jestch.2015.11.003.
Ming-Huwi Horng. (2012). “Vector quantization using the firefly algorithm for image compression”, Expert Systems with Applications vol 39 (2012) pp no-1078–1091. DOI: 10.1016/j.eswa.2011.07.108
Yan Wang, Xiao-Yue Feng, Yan-Xin Huang, Dong-Bing Pu, Wen-Gang Zhou, Yan-Chun Liang, Chun-Guang Zhou. (2007). “A novel quantum swarm evolutionary algorithm and its applications”, Neurocomputing 70 (2007) 633–640. DOI: 10.1016/j.neucom.2006.10.001.
Xubing Zhang1, Zequn Guan1, and Tianhong Gan. (2007). “Particle Swarm Optimization Applied to Image Vector Quantization”, LSMS 2007, LNBI 4689, pp. 507–515, 2007. © Springer-Verlag Berlin Heidelberg 2007.
Y. Linde, A. Buzo, and R. M. Gray. (1980). “An algorithm for vector quantizer design”, IEEE Transactions on Communications., vol. COM-28, no. 1, pp. 84_95, Jan. 1980
Ming-Huwi Horng. (2009). “Honey Bee Mating Optimization Vector Quantization Scheme in Image Compression”, AICI 2009, LNAI 5855, pp. 185–194, 2009. © Springer-Verlag Berlin Heidelberg 2009.
Jia Wen, Caiwen Ma and Penglang Shui. (2010). “An adaptive VQ method used on interferential multi-spectral image lossless compression”, Optics Communications 284 (2011) 64–73, ©Elsevier, doi:10.1016/j.optcom.2010.08.030.
Xun Jin and JongWeon Kim. (2012). “Imperceptibility Improvement of Image Watermarking Using Variance Selection”, SIP/WSE/ICHCI 2012, CCIS 342, pp. 31–38, 2012. © Springer-Verlag Berlin Heidelberg 2012.
S. N. Kumar, A. Lenin Fred, H. Ajay Kumar, P. Sebastin Varghese and Ashy V. Daniel. (2019). “BAT Optimization-Based Vector Quantization Algorithm for Compression of CT Medical Images”, ICTMI 2017, © Springer Nature Singapore Pte Ltd. 2019. https://DOI.org/10.1007/978-981-13-1477-3_5.
M. Laxmi Prasanna Rani1 · Gottapu Sasibhushana Rao2 · B. Prabhakara Rao. (2020). “An efficient codebook generation using firefly algorithm for optimum medical image compression”, Journal of Ambient Intelligence and Humanized Computing, February 2020, © Springer-Verlag GmbH Germany, part of Springer Nature 2020. https://DOI.org/10.1007/s12652-020-01782-w.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
