LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model
Abstract
Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
Keywords
Full Text:
PDFReferences
E. Hoogeboom, J. Peters, R. V. D. Berg, and M. Welling, “Integer discrete flows and lossless compression,” Advances in Neural Information Processing Systems, vol. 32, 2019, doi: 10.48550/arXiv.1905.07376.
M. Zhang, X. Tong, Z. Wang, and P. Chen, “Joint lossless image compression and encryption scheme based on calic and hyperchaotic system,” Entropy, vol. 23, no. 8, p. 1096, 2021, doi: 10.3390/e23081096.
H. Zhang, X.-Q. Wang, Y.-J. Sun, and X.-Y. Wang, “A novel method for lossless image compression and encryption based on lwt, spiht and cellular automata,” Signal Processing: Image Communication, vol. 84, p. 115829, 2020, doi: 10.1016/j.image.2020.115829.
M. A. Rahman and M. Hamada, “Lossless image compression techniques: A state-of- the-art survey,” Symmetry, vol. 11, no. 10, p. 1274, 2019, doi: 10.3390/sym11101274.
A. P. Singh, A. Potnis, and A. Kumar, “A review on latest techniques of image compression,” International Research Journal of Engineering and Technology (IRJET), vol. 3, no. 7, pp. 2395–0056, 2016.
H. Rhee, Y. I. Jang, S. Kim, and N. I. Cho, “Lossless image compression by joint prediction of pixel and context using duplex neural networks,” IEEE Access, vol. 9, pp. 86632–86645, 2021, doi: 10.1109/ACCESS.2021.3088936.
S. Gumus and F. Kamisli, “A learned pixel-by-pixel lossless image compression method with 59k parameters and parallel decoding,” arXiv preprint arXiv:2212.01185, 2022, doi: 10.48550/ arXiv.2212.01185.
J. Sneyers and P. Wuille, Flif: Free lossless image format based on maniac compression,” in 2016 IEEE International Conference on Image Processing (ICIP), pp. 66–70, 2016, doi: 10.1109/ICIP.2016.7532320.
M. J. Weinberger, G. Seroussi, and G. Sapiro, “The loco-i lossless image compression algorithm: Principles and standardization into jpeg-ls,” IEEE Transactions on Image processing, vol. 9, no. 8, pp. 1309–1324, 2000, doi: 10.1109/83.855427.
C. Christopoulos, A. Skodras, and T. Ebrahimi, “The jpeg2000 still image coding system: an overview,” IEEE transactions on consumer electronics, vol. 46, no. 4, pp. 1103–1127, 2000, doi: 10.1109/30.920468.
J. Ho, E. Lohn, and P. Abbeel, “Compression with flows via local bits-back coding,” Advances in Neural Information Processing Systems, vol. 32, 2019, doi: 10.48550/arXiv.1905.08500.
F. Kingma, P. Abbeel, and J. Ho, “Bit-swap: Recursive bits-back coding for lossless compression with hierarchical latent variables,” in International Conference on Machine Learning, pp. 3408–3417, 2019, doi: 10.48550/arXiv.1905.06845.
A. J. Hussain, D. Al-Jumeily, N. Radi, and P. Lisboa, “Hybrid neural network predictive-wavelet image compression system,” Neurocomputing, vol. 151, pp. 975–984, 2015, doi: 10.1016/j.neucom.2014.02.078.
B. Perumal and M. P. Rajasekaran, “A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression,” in 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pp. 1–5, 2016, doi: 10.1109/ICETETS.2016.7603060.
D. R. Hidalgo, B. B. Cortés, and E. C. Bravo, “Dimensionality reduction of hyperspec- tral images of vegetation and crops based on self-organized maps,” Information Processing in Agriculture, vol. 8, no. 2, pp. 310–327, 2021, doi: 10.1016/j.inpa.2020.07.002.
J. Han, “Texture image compression algorithm based on self-organizing neural network,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 4865808, 2022, doi: 10.1155/2022/4865808.
H. S. Malvar, G. J. Sullivan, and S. Srinivasan, “Lifting-based reversible color transformations for image compression,” Applications of digital image processing XXXI, vol. 7073, pp. 44–53, 2008, doi: 10.1117/12.797091.
R. Starosolski, “New simple and efficient color space transformations for lossless image compression,” Journal of Visual Communication and Image Representation, vol. 25, no. 5, pp. 1056–1063, 2014, doi: 10.1016/j.jvcir.2014.03.003.
S. Kim and N. I. Cho, “A lossless color image compression method based on a new reversible color transform,” in 2012 Visual Communications and Image Processing, pp. 1–4, 2012, doi: 10.1109/VCIP.2012.6410808.
M. Hernàndez-Cabronero, V. Sanchez, I. Blanes, F. Auli-Llinas, M. W. Marcellin, and J. Serra-Sagristà, “Mosaic-based color-transform optimization for lossy and lossy-to-lossless compression of pathology whole-slide images,” IEEE transactions on medical imaging, vol. 38, no. 1, pp. 21–32, 2018, doi: 10.1109/TMI.2018.2852685.
Y.-L. Lee and W.-H. Tsai, “A new secure image transmission technique via secret- fragment-visible mosaic images by nearly reversible color transformations,” IEEE Transactions on circuits and systems for video technology, vol. 24, no. 4, pp. 695–703, 2013, doi: 10.1109/TCSVT.2013.2283431.
L. F. Lucas, N. M. Rodrigues, L. A. Silva Cruz, and S. M. Faria, “Lossless compression of medical images using 3-d predictors,” IEEE transactions on medical imaging, vol. 36, no. 11, pp. 2250–2260, 2017, doi: 10.1109/TMI.2017.2714640.
X.-J. Tong, P. Chen, and M. Zhang, “A joint image lossless compression and encryp- tion method based on chaotic map,” Multimedia Tools and Applications, vol. 76, pp. 13995–14020, 2017, doi: 10.1007/s11042-016-3775-6.
S. A. Hassan and M. Hussain, “Spatial domain lossless image data compression method,” 2011 International Conference on Information and Communication Technologies, pp. 1–4, 2011, doi: 10.1109/ICICT.2011.5983563.
H. Zhang, F. Cricri, H. R. Tavakoli, N. Zou, E. Aksu, and M. M. Hannuksela, “Lossless image compression using a multi-scale progressive statistical model,” in Proceedings of the Asian Conference on Computer Vision, 2020, doi: 10.48550/arXiv.2108.10551.
G. Xin and P. Fan, “Soft compression for lossless image coding based on shape recognition,” Entropy, vol. 23, no. 12, p. 1680, 2021, doi: 10.3390/e23121680.
J. Abel, “Improvements to the burrows-wheeler compression algorithm: After bwt stages,” ACM Trans. Computer Systems, 2003.
M. A. Ali, A. Khan, M. Y. Javed, and A. Khanum, “Lossless image compression using kernel based global structure transform (gst),” in 2010 6th International Conference on Emerging Technologies (ICET), pp. 170–174, 2010, doi: 10.1109/ICET.2010.5638494.
A. Khan, A. Khan, M. Khan, and M. Uzair, “Lossless image compression: application of bi-level burrows wheeler compression algorithm (bbwca) to 2-d data,” Multimedia Tools and Applications, vol. 76, pp. 12391–12416, 2017, doi: 10.1007/s11042-016-3629-2.
X. Liu, P. An, Y. Chen, and X. Huang, “An improved lossless image compression algorithm based on huffman coding,” Multimedia Tools and Applications, vol. 81, no. 4, pp. 4781–4795, 2022, doi: 10.1007/s11042-021-11017-5.
N. Tumanyan, M. Geyer, S. Bagon, and T. Dekel, “Plug-and-play diffusion features for text-driven image-to-image translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1921-1930, 2023.
T. Zhou, Q. Li, H. Lu, Q. Cheng, and X. Zhang, “GAN review: Models and medical image fusion applications,” Information Fusion, vol. 91, pp. 134-148, 2023.
R. Kumar et al., “An integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals,” Computerized Medical Imaging and Graphics, vol. 87, p. 101812, 2021.
R. Mokady, A. Hertz, and A. H. Bermano, “Clipcap: Clip prefix for image captioning,” arXiv preprint arXiv:2111.09734, 2021.
M. Zhu et al., “Genimage: A million-scale benchmark for detecting ai-generated image,” Advances in Neural Information Processing Systems, vol. 36, 2024.
L. Guo et al., “Hydrogen safety: An obstacle that must be overcome on the road towards future hydrogen economy,” International Journal of Hydrogen Energy, vol. 51, pp. 1055-1078, 2024.
F. Xu, L. Huang, X. Gao, T. Yu, and L. Zhang, “Research on yolov3 model compression strategy for uav deployment,” Cognitive Robotics, vol. 4, pp. 8-18, 2024.
Z. R. Mohammed, Z. A. Yakoob, and M. Rajih, “Hybrid color image compression based on FMM and Huffman encoding techniques,” in AIP Conference Proceedings, vol. 3009, no. 1, 2024.
P. Samarathunga, V. Gowrisetty, T. Fernando, Y. Ganearachchi, and A. Fernando, “Region of interest scalable image compression using semantic communications,” in 2024 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-5, 2024.
B. Chidirala and B. Acharya, “Run length encoding based reversible data hiding scheme in encrypted images,” Journal of Electronic Imaging, vol. 33, no. 1, pp. 013018-013018, 2024.
V. I. Ungureanu, P. Negirla, and A. Korodi, “Image-Compression Techniques: Classical and “Region-of-Interest-Based” Approaches Presented in Recent Papers,” Sensors, vol. 24, no. 3, p. 791, 2024.
A. Mefoued, N. Kouadria, S. Harize, and N. Doghmane, “Improved discrete Tchebichef transform approximations for efficient image compression,” Journal of Real-Time Image Processing, vol. 21, no. 1, p. 12, 2024.
S. Subramani, G. Thirugnanam, N. Prabakaran, and J. B. Fernandes, “Robust medical image watermarking technique using integer wavelet transform and shearlet transform with BSVD,” International Journal of Advanced Intelligence Paradigms, vol. 27, no. 1, pp. 72-81, 2024.
Q. Sima, H. Feng, and B. Hu, “Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images,” Applied Sciences, vol. 14, no. 5, p. 1861, 2024.
Y. Xia et al., “Multi-scale architectures matter: Examining the adversarial robustness of flow-based lossless compression,” Pattern Recognition, vol. 149, p. 110242, 2024.
Y. Tian et al., “Intelligent reconstruction algorithm of hydrogen-fueled scramjet combustor flow based on knowledge distillation model compression,” International Journal of Hydrogen Energy, vol. 49, pp. 1278-1291, 2024.
P. A. Hsieh and J. L. Wu, “A review of the asymmetric numeral system and its applications to digital images,” Entropy, vol. 24, no. 3, p. 375, 2022.
C. Su et al., “Analysis of pre-processing methods for lossless compression of multi component medical images based on latent variable models,” in Laser Science, pp. JW5B-53, 2022.
C. B. Pande, S. A. Kadam, R. Jayaraman, S. Gorantiwar, and M. Shinde, “Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods,” Journal of the Saudi Society of Agricultural Sciences, vol. 21, no. 1, pp. 21-28, 2022.
A. Rajput, J. Li, F. Akhtar, Z. Hussain Khand, J. C. Hung, Y. Pei, and A. Börner, “A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage,” International Journal of Distributed Sensor Networks, vol. 18, no. 3, 2022.
C. Gnanavel, A. Johny Renoald, S. Saravanan, K. Vanchinathan, and P. Sathishkhanna, “An Experimental Investigation of Fuzzy‐Based Voltage‐Lift Multilevel Inverter Using Solar Photovoltaic Application,” Smart Grids and Green Energy Systems, pp. 59-74, 2022.
L. Zhang, Y. Wang, and D. Zhang, “Research on multiple-image encryption mechanism based on Radon transform and ghost imaging,” Optics communications, vol. 504, p. 127494, 2022.
R. Li et al., “A Real-Time Incremental Video Mosaic Framework for UAV Remote Sensing,” Remote Sensing, vol. 15, no. 8, p. 2127, 2023.
T. Wang, H. Wang, K. Wang, and Z. Yang, “Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag,” Applied Sciences, vol. 13, no. 2, p. 756, 2023.
R. K. Kanna, A. Ambikapathy, M. R. Al-Hameed, V. V. Reddy, and N. Singh, “Modern 3d Compression Application in Medical Imaging Approach,” in 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), vol. 10, pp. 1492-1496, 2023.
T. H. Park and S. D’Amico, “Adaptive Neural-Network-Based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft,” Journal of Guidance, Control, and Dynamics, vol. 46, no. 9, pp. 1671-1688, 2023.
M. Waleed, T. W. Um, A. Khan, and A. Khan, “On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression,” Applied Sciences, vol. 10, no. 3, p. 937, 2020.
J. Uthayakumar, M. Elhoseny, and K. Shankar, “Highly reliable and low-complexity image compression scheme using neighborhood correlation sequence algorithm in WSN,” IEEE Transactions on Reliability, vol. 69, no. 4, pp. 1398-1423, 2020.
M. Alqerom. An intelligent system for the classification and selection of novel and efficient lossless image compression algorithms. University of Salford (United Kingdom), 2020.
A. Shalayiding, Z. Arnavut, B. Koc, and H. Kocak, “Burrows-Wheeler Transformation for Medical Image Compression,” in 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0723-0727, 2020.
https://links.uwaterloo.ca/Repository.html.
https://www.kaggle.com/datasets/sherylmehta/kodak-dataset.
http://documents.epfl.ch/groups/g/gr/gr-eb-unit/www/IQA/Original.zip.
DOI: https://doi.org/10.18196/jrc.v5i5.21834
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Mahmoud Al Qerom, Mohammad Otair, Farid Meziane, Sawsan AbdulRahman, Maen Alzubi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Journal of Robotics and Control (JRC)
P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com