Comparative Analysis of Lossless Image Compression Algorithms

Authors

  • Umer Ijaz Department of Electrical Engineering & Technology, GC University, Faisalabad
  • Abubaker Ijaz Development WASA, Faisalabad
  • Ali Iqbal Department of Electrical Engineering & Technology, GC University, Faisalabad
  • Fouzia Gillani Department of Mechanical Engineering and Technology, GC University, Faisalabad
  • Muzammil Hayat Department of Electrical Engineering & Technology, GC University, Faisalabad

Keywords:

Lossless Image Compression, Run-Length Encoding, Differential Pulse Code Modulation, Burrows-Wheeler Transform, PSNR, SSIM, MSE, RMSE, Bitrate, Computational Complexity

Abstract

This research paper conducts a comprehensive analysis of three key lossless image compression algorithms: Run-Length Encoding (RLE), Burrows-Wheeler Transform (BWT) and Differential Pulse Code Modulation (DPCM).  The increasing demand for efficient image storage and transmission necessitates a thorough examination of these algorithms.  Lossless compression plays a crucial role in diminishing data redundancy while safeguarding the integrity and quality of images. The study encompasses data collection, performance metrics, and algorithm evaluation. Results reveal the strengths and weaknesses of each algorithm. RLE excels in image quality preservation but may not achieve the highest compression ratios. DPCM provides a compromise between resource-efficient compression and image fidelity. BWT offers a competitive balance between compression efficiency and image quality. Based on the comprehensive analysis of three key lossless image compression algorithms, it was observed that BWT emerges as a versatile choice that offers competitive compression while maintaining reasonable image quality.  However, when choosing the most suitable algorithm, it is essential to consider specific application requirements, including the desired level of image quality preservation and the availability of computational resource.

Author Biographies

Umer Ijaz, Department of Electrical Engineering & Technology, GC University, Faisalabad

Assistant Professor,
Department of Electrical Engineering,
Government College University, Faisalabad

Abubaker Ijaz, Development WASA, Faisalabad

Director Development
WASA, Faisalabad

Fouzia Gillani, Department of Mechanical Engineering and Technology, GC University, Faisalabad

Department of Mechanical Engineering and Technology
Government College University, Faisalabad.

Muzammil Hayat, Department of Electrical Engineering & Technology, GC University, Faisalabad

Department of Electrical Engineering and Technology
Government College University, Faisalabad.

References

M. A. Rahman, M. Hamada, and M. A. Rahman, “A comparative analysis of the state-of-the-art lossless image compression techniques,” SHS Web Conf., vol. 139, p. 03001, 2022, doi: 10.1051/SHSCONF/202213903001.

N. A. N. Azman, S. Ali, R. A. Rashid, F. A. Saparudin, and M. A. Sarijari, “A hybrid predictive technique for lossless image compression,” Bull. Electr. Eng. Informatics, vol. 8, no. 4, pp. 1289–1296, Dec. 2019, doi: 10.11591/EEI.V8I4.1612.

R. Naveen Kumar, B. N. Jagadale, and J. S. Bhat, “A lossless image compression algorithm using wavelets and fractional Fourier transform,” SN Appl. Sci., vol. 1, no. 3, pp. 1–8, Mar. 2019, doi: 10.1007/S42452-019-0276-Z/FIGURES/8.

M. A. Rahman, M. Hamada, and J. Shin, “The Impact of State-of-the-Art Techniques for Lossless Still Image Compression,” Electron. 2021, Vol. 10, Page 360, vol. 10, no. 3, p. 360, Feb. 2021, doi: 10.3390/ELECTRONICS10030360.

M. A. Rahman and M. Hamada, “PCBMS: A Model to Select an Optimal Lossless Image Compression Technique,” IEEE Access, vol. 9, pp. 167426–167433, 2021, doi: 10.1109/ACCESS.2021.3137345.

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,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12624 LNCS, pp. 609–622, 2021, doi: 10.1007/978-3-030-69535-4_37/COVER.

R. Suresh Kumar and P. Manimegalai, “Near lossless image compression using parallel fractal texture identification,” Biomed. Signal Process. Control, vol. 58, p. 101862, Apr. 2020, doi: 10.1016/J.BSPC.2020.101862.

M. Otair, L. Abualigah, and M. K. Qawaqzeh, “Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression,” Multimed. Tools Appl., vol. 81, no. 20, pp. 28509–28529, Aug. 2022, doi: 10.1007/S11042-022-12846-8/METRICS.

M. A. Rahman and M. Hamada, “A prediction-based lossless image compression procedure using dimension reduction and Huffman coding,” Multimed. Tools Appl., vol. 82, no. 3, pp. 4081–4105, Jan. 2023, doi: 10.1007/S11042-022-13283-3/METRICS.

M. A. Rahman and M. Hamada, “A Semi-lossless image compression procedure using a lossless mode of jpeg,” Proc. - 2019 IEEE 13th Int. Symp. Embed. Multicore/Many-Core Syst. MCSoC 2019, pp. 143–148, Oct. 2019, doi: 10.1109/MCSOC.2019.00028.

S. C. Satapathy, V. Bhateja, M. Ramakrishna Murty, N. Gia Nhu, and Jayasri Kotti, Eds., “Communication Software and Networks,” vol. 134, 2021, doi: 10.1007/978-981-15-5397-4.

M. A. Rahman and M. Hamada, “Lossless Image Compression Techniques: A State-of-the-Art Survey,” Symmetry 2019, Vol. 11, Page 1274, vol. 11, no. 10, p. 1274, Oct. 2019, doi: 10.3390/SYM11101274.

M. A. Al-jawaherry and S. Y. Hamid, “Image Compression Techniques: Literature Review,” J. Al-Qadisiyah Comput. Sci. Math., vol. 13, no. 4, p. Page 10-21-Page 10 – 21, Dec. 2021, doi: 10.29304/JQCM.2021.13.4.860.

L. S. S. P. Amandeep Kaur, Sonali Gupta, “COMPREHENSIVE STUDY OF IMAGE COMPRESSION TECHNIQUES,” J. Crit. Rev, vol. 7, no. 17, pp. 2382–2388, 2020.

Y. L. Prasanna, Y. Tarakaram, Y. Mounika, and R. Subramani, “Comparison of Different Lossy Image Compression Techniques,” Proc. 2021 IEEE Int. Conf. Innov. Comput. Intell. Commun. Smart Electr. Syst. ICSES 2021, 2021, doi: 10.1109/ICSES52305.2021.9633800.

A. K. Singh, S. Bhushan, and S. Vij, “A Brief Analysis and Comparison of DCT- and DWT-Based Image Compression Techniques,” pp. 45–55, 2021, doi: 10.1007/978-981-15-4936-6_5.

A. Birajdar, H. Agarwal, M. Bolia, and V. Gupte, “Image Compression using Run Length Encoding and its Optimisation,” 2019 Glob. Conf. Adv. Technol. GCAT 2019, Oct. 2019, doi: 10.1109/GCAT47503.2019.8978464.

K. L. Precious, G.B. and Giok, “A COMPARATIVE ANALYSIS OF IMAGE COMPRESSION USING PCM AND DPCM,” Inf. Technol., vol. 4, no. 1, pp. 60–67, 2020.

G. A. Haidar, R. Achkar, and H. Dourgham, “A comparative simulation study of the real effect of PCM, DM and DPCM systems on audio and image modulation,” 2016 IEEE Int. Multidiscip. Conf. Eng. Technol. IMCET 2016, pp. 144–149, Dec. 2016, doi: 10.1109/IMCET.2016.7777442.

Z. H. Abeda and G. K. AL-Khafaji, “Pixel Based Techniques for Gray Image Compression: A review,” J. Al-Qadisiyah Comput. Sci. Math., vol. 14, no. 2, p. Page 59-70, Jul. 2022, doi: 10.29304/JQCM.2022.14.2.967.

A. Shalayiding, Z. Arnavut, B. Koc, and H. Kocak, “Burrows-Wheeler Transformation for Medical Image Compression,” 11th Annu. IEEE Inf. Technol. Electron. Mob. Commun. Conf. IEMCON 2020, pp. 723–727, Nov. 2020, doi: 10.1109/IEMCON51383.2020.9284917.

“Burrows Wheeler transform - Wikipedia.” Accessed: Nov. 06, 2023. [Online]. Available: https://en.wikipedia.org/wiki/Burrows_Wheeler_transform

M. B. Begum, N. Deepa, M. Uddin, R. Kaluri, M. Abdelhaq, and R. Alsaqour, “An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm,” Heliyon, vol. 9, no. 6, Jun. 2023, doi: 10.1016/j.heliyon.2023.e17602.

G. Devika, R. Sandha, S. Shaik Parveen, and P. Hemavathy, “BURROWS WHEELER TRANSFORM FOR SATELLITE IMAGE COMPRESSION USING WHALE OPTIMIZATION ALGORITHM,” Adv. Appl. Math. Sci., vol. 20, no. 11, pp. 2627–2634, 2021.

Č. Livada, T. Horvat, and A. Baumgartner, “Novel Block Sorting and Symbol Prediction Algorithm for PDE-Based Lossless Image Compression: A Comparative Study with JPEG and JPEG 2000,” Appl. Sci. 2023, Vol. 13, Page 3152, vol. 13, no. 5, p. 3152, Feb. 2023, doi: 10.3390/APP13053152.

“SIPI Image Database - Misc.” Accessed: Nov. 06, 2023. [Online]. Available: https://sipi.usc.edu/database/database.php?volume=misc

H. Choi and I. V. Bajic, “Scalable Image Coding for Humans and Machines,” IEEE Trans. Image Process., vol. 31, pp. 2739–2754, 2022, doi: 10.1109/TIP.2022.3160602.

N. Le, H. Zhang, F. Cricri, R. Ghaznavi-Youvalari, H. R. Tavakoli, and E. Rahtu, “LEARNED IMAGE CODING FOR MACHINES: A CONTENT-ADAPTIVE APPROACH,” Proc. - IEEE Int. Conf. Multimed. Expo, 2021, doi: 10.1109/ICME51207.2021.9428224.

T. Chen, H. Liu, Z. Ma, Q. Shen, X. Cao, and Y. Wang, “End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling,” IEEE Trans. Image Process., vol. 30, pp. 3179–3191, 2021, doi: 10.1109/TIP.2021.3058615.

F. Yuan, L. Zhan, P. Pan, and E. Cheng, “Low bit-rate compression of underwater image based on human visual system,” Signal Process. Image Commun., vol. 91, p. 116082, Feb. 2021, doi: 10.1016/J.IMAGE.2020.116082.

S. Cho et al., “Low Bit-rate Image Compression based on Post-processing with Grouped Residual Dense Network”.

A. Lin, B. Chen, J. Xu, Z. Zhang, G. Lu, and D. Zhang, “DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation,” IEEE Trans. Instrum. Meas., vol. 71, 2022, doi: 10.1109/TIM.2022.3178991.

W. Wang, C. Chen, M. Ding, H. Yu, S. Zha, and J. Li, “TransBTS: Multimodal Brain Tumor Segmentation Using Transformer,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12901 LNCS, pp. 109–119, 2021, doi: 10.1007/978-3-030-87193-2_11/COVER.

S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M. H. Yang, “Restormer: Efficient Transformer for High-Resolution Image Restoration,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2022-June, pp. 5718–5729, 2022, doi: 10.1109/CVPR52688.2022.00564.

A. Hatamizadeh et al., “UNETR: Transformers for 3D Medical Image Segmentation,” Proc. - 2022 IEEE/CVF Winter Conf. Appl. Comput. Vision, WACV 2022, pp. 1748–1758, 2022, doi: 10.1109/WACV51458.2022.00181.

J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V. M. Patel, “Medical Transformer: Gated Axial-Attention for Medical Image Segmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12901 LNCS, pp. 36–46, 2021, doi: 10.1007/978-3-030-87193-2_4/COVER.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, Jul. 2021, doi: 10.7717/PEERJ-CS.623/SUPP-1.

H. Singh, A. S. Ahmed, F. Melandsø, and A. Habib, “Ultrasonic image denoising using machine learning in point contact excitation and detection method,” Ultrasonics, vol. 127, p. 106834, Jan. 2023, doi: 10.1016/J.ULTRAS.2022.106834.

A. Kumar and M. Dua, “Image encryption using a novel hybrid chaotic map and dynamic permutation−diffusion,” Multimed. Tools Appl., pp. 1–24, Sep. 2023, doi: 10.1007/S11042-023-16817-5/METRICS.

Y. Lu, M. Gong, L. Cao, Z. Gan, X. Chai, and A. Li, “Exploiting 3D fractal cube and chaos for effective multi-image compression and encryption,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, no. 3, pp. 37–58, Mar. 2023, doi: 10.1016/J.JKSUCI.2023.02.004.

O. Rashid, A. Amin, and M. R. Lone, “Performance analysis of DWT families,” Proc. 3rd Int. Conf. Intell. Sustain. Syst. ICISS 2020, pp. 1457–1463, Dec. 2020, doi: 10.1109/ICISS49785.2020.9315960.

U. Sara, M. Akter, M. S. Uddin, U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,” J. Comput. Commun., vol. 7, no. 3, pp. 8–18, Mar. 2019, doi: 10.4236/JCC.2019.73002.

Y. Huang, B. Niu, H. Guan, and S. Zhang, “Enhancing Image Watermarking with Adaptive Embedding Parameter and PSNR Guarantee,” IEEE Trans. Multimed., vol. 21, no. 10, pp. 2447–2460, Oct. 2019, doi: 10.1109/TMM.2019.2907475.

U. Erkan, D. N. H. Thanh, L. M. Hieu, and S. Enginoglu, “An iterative mean filter for image denoising,” IEEE Access, vol. 7, pp. 167847–167859, 2019, doi: 10.1109/ACCESS.2019.2953924.

A. Elhadad, A. Ghareeb, and S. Abbas, “A blind and high-capacity data hiding of DICOM medical images based on fuzzification concepts,” Alexandria Eng. J., vol. 60, no. 2, pp. 2471–2482, Apr. 2021, doi: 10.1016/J.AEJ.2020.12.050.

W. Chen, B. Qi, X. Liu, H. Li, X. Hao, and Y. Peng, “Temperature-Robust Learned Image Recovery for Shallow-Designed Imaging Systems,” Adv. Intell. Syst., vol. 4, no. 10, p. 2200149, Oct. 2022, doi: 10.1002/AISY.202200149.

W. Y. Juan, “Generating Synthesized Computed Tomography (CT) from Magnetic Resonance Imaging Using Cycle-Consistent Generative Adversarial Network for Brain Tumor Radiation Therapy,” Int. J. Radiat. Oncol. Biol. Physics, vol. 111, no. 3, pp. e111–e11, 2021.

D. R. I. M. Setiadi, “PSNR vs SSIM: imperceptibility quality assessment for image steganography,” Multimed. Tools Appl., vol. 80, no. 6, pp. 8423–8444, Mar. 2021, doi: 10.1007/S11042-020-10035-Z/METRICS.

J. Nilsson and T. Akenine-Möller, “Understanding SSIM,” Jun. 2020, Accessed: Nov. 06, 2023. [Online]. Available: https://arxiv.org/abs/2006.13846v2

V. V. Starovoitov, E. E. Eldarova, and K. T. Iskakov, “Comparative analysis of the ssim index and the pearson coefficient as a criterion for image similarity,” Eurasian J. Math. Comput. Appl., vol. 8, no. 1, pp. 76–90, 2020, doi: 10.32523/2306-6172-2020-8-1-76-90.

J. Peng et al., “Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection,” Med. Phys., vol. 47, no. 4, pp. 1907–1919, Apr. 2020, doi: 10.1002/MP.14010.

Downloads

Published

2023-11-12

How to Cite

Ijaz, U., Ijaz, A., Iqbal, A., Gillani, F., & Hayat, M. (2023). Comparative Analysis of Lossless Image Compression Algorithms. International Journal of Innovations in Science & Technology, 5(4), 548–561. Retrieved from https://journal.50sea.com/index.php/IJIST/article/view/559

Issue

Section

Articles