An Extensive Analysis of Digital Image Compression Techniques Using Different Image Files and Color Formats

Fauziah (1), Dhieka Avrilia Lantana (2), Nurhayati (3), Ira Diana Sholihati (4), Ratih Titi Komala Sari (5), Billy Hendrik (6)
(1) Faculty of Computer Science, Universitas Nasional, Indonesia
(2) Faculty of Computer Science, Universitas Nasional, Indonesia
(3) Faculty of Computer Science, Universitas Nasional, Indonesia
(4) Faculty of Computer Science, Universitas Nasional, Indonesia
(5) Faculty of Computer Science, Universitas Nasional, Indonesia
(6) Faculty of Computer Science, Universitas Putra Indonesia “YPTK” Padang, Padang, Indonesia
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How to cite (IJASEIT) :
Fauziah, et al. “An Extensive Analysis of Digital Image Compression Techniques Using Different Image Files and Color Formats”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 5, Oct. 2023, pp. 1971-7, doi:10.18517/ijaseit.13.5.19319.
Data storage on the device can affect the access speed of the device used; for example, files, images, and data will affect the performance of the device, become slow to access, difficult to open, download, and save images, files, because the available storage capacity is limited, with the problems that arise, an image compression technique is needed to minimize storage space and speed up the access. The compression technique can reduce a file, image, and data size but does not reduce the existing image's quality or lower the threshold during the sending or receiving. This research aims to reduce the size, speed up the process of accessing data on devices, and, more importantly, minimize memory space. It can also affect the bandwidth used when sending and receiving files and can speed up the process of sending from source to destination. The method used in this study is Lossy Compression, lose less Compression by comparing RLE, Huffman, and LZW using different image file types. For the Lossless Technique, the best quality reduction ratio is in binary image types, whether using a background or not using a background. The best results obtained are 99.10% (PNG Compression). Using the BMP file extension type, the recommended reduced ratio is lossy compression with format image BMP (JPG compression) for binary image using Lossless Compression has a good reduced ratio compression with an average of 99%.

Yitao Huang “Overview of Research Progress of Digital Image Processing Technology”, CONF-CIAP 2022 Journal of Physics: Conference Series 2386, 2022012034 IOP Publishing, Vol.2386, pp.1-7, December, 2022, doi:10.1088/1742-6596/2386/1/012034

Jun-Hyuk Kim, Soobeom Jang, Jun-Ho Choi, Jong-Seok Lee,”Successive learned image compression: Comprehensive analysis of instability”, Neurocomputing, Volume 506, September 2022, pp. 12-24,

Saeed Ranjbar, Alvar Mateen Ulhaq Hyomin Choi, Ivan V. Bajić*, Front “Joint image compression and denoising via latent-space scalability”,. Signal Process., Sec. Image Processing, Volume 2 , pp.1-17, September 2022|

R. M. Al-Saleem, Y. A. Ghani, and S. A. Shawkat, “Improvement of Image Compression by Changing the Mathematical Equation Style in Communication Systems,” International Journal of Digital Multimedia Broadcasting, vol. 2022, no.1, pp. 1-7, Nov. 2022,.

A. Rahman, M. Hamada, and A. Rahman, “A comparative analysis of the state-of-the-art lossless image compression techniques,” SHS Web of Conferences, vol. 139, pp. 1-6, May 2022, doi: 10.1051/shsconf/202213903001.

Z. Ma, H. Zhu, Z. He, Y. Lu, and F. Song, “Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data,” Sensors, vol. 22, no. 14, p. 5331-5340, Jul. 2022, doi: 10.3390/s22145331.

A. Almaliki, F. Alyousuf, and R. Din, “Review on techniques and file formats of image compression,” Bulletin of Electrical Engineering and Informatics, vol. 9, pp. 602-610, Apr. 2020, doi: 10.11591/eei.v9i2.2085.

C. Oswald, E. Haritha, A. Akash Raja, B. Sivaselvan, “An efficient and novel data clustering and run length encoding approach to image compression,” Volume33, Issue10, 25 May 2021,

Mohammed Otair,Osama Abdulraziq Hasan, Laith Abualigah, The effect of using minimum decreasing techniqueon enhancing the quality of lossy compressed images, Multimedia Tools and Applications (2023) Vol.82:pp. 4107-4138, Juli, 2022,

Dankan Gowda, Avinash Sharma Rajesh Lc, Mirzanur Rahman, Ghazaala Yasmin, Parismita Sarma, A. Azhagu Jaisudhan Pazhani, A novel method of data compression using ROI for biomedical 2D images, Measurement: Sensors, Volume 24, December 2022, 100439,

Shaowen Ma, “Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms” Proceedings of the 3rd International Conference on Signal Processing and Machine Learning, pp.793-801, June 2023, DOI: 10.54254/2755-2721/5/20230705

S. Janarthanan and U. Naha, "An Analysis on Techniques of Image Compression Lossy And Lossless," 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 1-5, July 2022, doi: 10.1109/ICERECT56837.2022.10060123

S. Yamagiwa, W. Yang, and K. Wada, “Adaptive Lossless Image Data Compression Method Inferring Data Entropy by Applying Deep Neural Network,” Electronics (Basel), vol. 11, no. 4, p. 504, Feb. 2022, doi: 10.3390/electronics11040504.

F. Mentzer, L. Van Gool, and M. Tschannen, “Learning Better Lossless Compression Using Lossy Compression,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2020, pp. 6637-6646. DOI: 10.1109/CVPR42600.2020.00667

S. Kumar and D. Kumar, “Comparative Analysis and Performance Evaluation of Medical Image Compression Method for Telemedicine,” in 2nd International Conference on Data, Engineering and Applications (IDEA), IEEE, Feb. 2020, pp. 1-5. doi: 10.1109/IDEA49133.2020.9170724.

P. Dahiwal and A. Kulkarni, “An Analytical Survey on Image Compression,” in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), IEEE, pp. 656-661. Jul. 2020, doi: 10.1109/WorldS450073.2020.9210364.

O. Sudana, D. Witarsyah, A. Putra, and S. Raharja, “Mobile Application for Identification of Coffee Fruit Maturity using Digital Image Processing,” Int J Adv Sci Eng Inf Technol, vol. 10, no. 3, p. 980, Jun. 2020, doi: 10.18517/ijaseit.10.3.11135.

A. R. Idrais, I. Aljarrah, and O. Al-Khaleel, “A Spatial Image Compression Algorithm based on Run Length Encoding,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2607-2616, Oct. 2021, doi: 10.11591/eei.v10i5.2563.

Liu, Xiaoxiao, Ping An, Yilei Chen and Xinpeng Huang. “An improved lossless image compression algorithm based on Huffman coding.” Multimedia Tools and Applications 81 (2021): Juni, 2021, pp. 4781 - 4795.

S. Biswas, T. Ghosh and S. Nath, "Selective Run-Length Constrained Encoding Scheme on Extended Nucleic Acid Memory," 2022 IEEE VLSI Device Circuit and System (VLSI DCS), pp. 148-153, July 2022, doi: 10.1109/VLSIDCS53788.2022.9811440..

M. R. Mufid et al., “Image Data Compression in the Public Reporting System in Lamongan using the Huffman Method and Run Length Encoding,” in Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021), Atlantis Press,pp.887-891, March 2022 doi: 10.2991/assehr.k.220301.146.

M. Gashnikov, "Choosing Machine Learning Methods for Image Compression," 2022 VIII International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russian Federation, pp. 1-4, August 2022, doi: 10.1109/ITNT55410.2022.9848706

Cunha, F.F., Blí¼ml, V., Zopf, L.M. et al. Correction to: Lossy Image Compression in a Preclinical Multimodal Imaging Study. J Digit Imaging, Vol 1, pp 67-86,July 2023,

Jianmin Han, “ Texture Image Compression Algorithm Based on Self-Organizing Neural Network”,Computational Intelligence and Neuroscience,Vol.2022,April 2022, pp. 1-10,

W. Xiao, N. Wan, A. Hong, and X. Chen, “A Fast JPEG Image Compression Algorithm Based on DCT,” in 2020 IEEE International Conference on Smart Cloud (SmartCloud), IEEE,.pp. 106-110. Nov. 2020, doi: 10.1109/SmartCloud49737.2020.00028.

Alzahrani, M., & Albinali, M. Comparative Analysis of Lossless Image Compression Algorithms based on Different Types of Medical Images. 2021 International Conference of Women in Data Science at Taif University (WiDSTaif), pp. 1-6, March 2021, doi:10.1109/widstaif52235.2021.9430242

Y. L. Prasanna, Y. Tarakaram, Y. Mounika and R. Subramani, "Comparison of Different Lossy Image Compression Techniques," 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), December 2021, pp. 1-7, doi: 10.1109/ICSES52305.2021.9633800.

Y. L. Prasanna, Y. Tarakaram, Y. Mounika and R. Subramani, "Comparison of Different Lossy Image Compression Techniques," 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, December 2021, pp. 1-7, doi: 10.1109/ICSES52305.2021.9633800., DOI: 10.1109/ICSES52305.2021.9633800

Sonain Jamil, Md.Jalil Piran , MuhibUr Rahman, Oh-Jin Kwon, Learning-driven lossy image compression: A comprehensive survey, Engineering Applications of Artificial Intelligence, Volume 123, Part B, August2023, 106361,

H. Kanagaraj and V. Muneeswaran, “Image compression using HAAR discrete wavelet transform,” in 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), IEEE, Mar. 2020, pp. 271-274. doi: 10.1109/ICDCS48716.2020.243596.

A. G. Alkholidi, "An Advanced Approach for Optical Large Size Colored Image Compression Using RGB Laser Beams: Simulation Results," 2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI), Sana'a, Yemen, January 2022, pp. 1-7, doi: 10.1109/MTICTI53925.2021.9664787.

B. Li, J. Liang and J. Han, "Variable-Rate Deep Image Compression With Vision Transformers," in IEEE Access, vol. 10, pp. 50323-50334, May 2022, doi: 10.1109/ACCESS.2022.3173256.

Dipti Mishra, Satish Kumar Singh, Rajat Kumar Singh, Deep Architectures for Image Compression: A Critical Review, vol.191, September 2021,

D. Barman* and M. B. Ahamed, “Improved LZW Compression Technique using Difference Method,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 5, pp. 87-92, Mar. 2020, doi: 10.35940/ijitee.E2216.039520.

Md. Atiqur Rahman , Mohamed Hamada and Md Asfaqur Rahman, “A comparative analysis of the state-of-the-art lossless image compression techniques”, SHS Web of Conferences Vol.139, pp.1-5, May 2022,

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

Marlapalli, Krishna, Rani S. B. P. Bandlamudi, Rambabu Busi, Vallabaneni Pranav, and B. Madhavrao. “A Review on Image Compression Techniques.” Communication Software and Networks, 2020,Conference paper, Vol.134, pp.271-79. Oct 2020, doi:10.1007/978-981-15-5397-4_29.

Y. Mikami, C. Tsutake, K. Takahashi, and T. Fujii, “An Efficient Image Compression Method Based On Neural Network: An Overfitting Approach,” in 2021 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 2084-2088. Sep. 2021, doi: 10.1109/ICIP42928.2021.9506367.

Zhongqiang Li, Alexandra Ramos, Zheng Li, Michelle L. Osborn, Xin Li, Yanping Li, Shaomian Yao, Jian Xu,”An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image”, Biomedical Signal Processing and Control, Volume 64, pp. 102306, February 2021,

A. H. M. Z. Karim, Md. S. Miah, M. A. Al Mahmud, and M. T. Rahman, “Image Compression using Huffman Coding Scheme with Partial/Piecewise Color Selection,” in 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), IEEE, Sep. 2021, pp. 1-6. doi: 10.1109/GUCON50781.2021.9573863

B. F. A. B H and P. R, “Overview on Machine Learning in Image Compression Techniques,” in 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, Nov. 2021, pp. 1-8. doi: 10.1109/i-PACT52855.2021.9696987.

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