Perancangan Aplikasi Kompresi Citra dengan Algoritma Embedded Zero Tree Wavelet
DOI:
https://doi.org/10.64366/adajisr.v2i2.76Keywords:
Application Design; Image Compression; Embedded Algorithm; Decision Tree WavaletAbstract
The Embedded Zero-Tree Wavelet (EZW) algorithm is a wavelet-based image compression method that offers high-efficiency progressive encoding through the use of a zero-tree structure to represent insignificant wavelet coefficients. This study designs and implements an EZW-based image compression application to address the main problem of image compression, which is finding a balance between a high compression ratio and image reconstruction quality that is still acceptable to the human visual system. The research stages included literature study, algorithm and interface design, program code implementation, and testing using various test images. The results showed that the EZW algorithm was able to achieve a compression ratio of between 87.90% and 93.13%. The compression results showed a significant reduction in file size. For example, an image sized 2311 KB could be compressed to 191 KB, while maintaining good visual quality based on the Peak Signal-to-Noise Ratio (PSNR) and visual evaluation. Compared to conventional methods such as JPEG and Discrete Cosine Transform (DCT), EZW proved to be superior in bandwidth efficiency and artefact reduction in images with high compression rates. This study also identified a research gap in computational efficiency and power consumption when the algorithm is applied to real-time systems, thus requiring further development in hardware optimisation and quantisation stage modification. Thus, this study confirms that EZW-based image compression applications can be an effective solution for digital image storage and transmission needs in environments with limited bandwidth and storage capacity.
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References
T. C. T. Jenny and G. MuthuLakshmi, “A Modified Embedded Zero-Tree Wavelet Method for Medical Image Compression,” ICTACT J. Image Video Process., vol. 1, no. 2, pp. 87–91, 2010, doi: https://doi.org/10.21917/IJIVP.2010.0013.
A. Ouafi, Z.-E. Baarir, A. Taleb-Ahmed, N. Doghmane, and A. Zitouni, “New approach based on Shapiro’s embedded zero-tree wavelet algorithm for image compression,” Opt. Eng., vol. 46, no. 7, p. 77008, 2007, doi: https://doi.org/10.1117/1.2747590.
A. K. Al-Selifani and F. A. Mustafa, “Compression of Satellites Images Using Embedded Zero Tree Wavelet,” AL-Rafidain J. Comput. Sci. Math., vol. 3, no. 2, pp. 89–102, 2006.
Z. Chen, C. Mu, and F. Xu, “An improvement of embedded zerotree wavelet coding based on compressed sensing,” in 2014 IEEE 5th International Conference on Software Engineering and Service Science, IEEE, 2014, pp. 1177–1180. doi: 10.1109/ICSESS.2014.6933776.
A. Manduca, “Embedded zerotree wavelet compression of medical images,” in Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society, IEEE, 1995, pp. 441–442. doi: 10.1109/IEMBS.1995.575190.
R. K. Ingole, “Embedded Image Compression: A Review,” Int. J. Data Sci. Anal., vol. 3, no. 1, pp. 1–4, 2017, doi: 10.11648/j.ijdsa.20170301.11.
R. Naveen Kumar, “An efficient image compression using modified embedded zero tree coding with SVD,” Multimed. Tools Appl., vol. 83, no. 13, pp. 37795–37812, 2024, doi: https://doi.org/10.1007/s11042-023-16725-8.
J. Zhang, Y. Lu, T. Li, and G. Lei, “Study on the application of embedded zero-tree wavelet algorithm in still images compression,” in ICMIT 2005: Information Systems and Signal Processing, SPIE, 2006, pp. 93–97. doi: https://doi.org/10.1117/12.664294.
S. Alfarisi, R. M. R. Sitanggang, and A. Christina, “Applied Algebra for Image Compression: A Systematic Literature Review,” J. Ilmu Komput. dan Inform., vol. 4, no. 2, pp. 117–126, 2024, doi: https://doi.org/10.54082/jiki.208.
M. F. Febriansyah and T. Sutabri, “Kompresi dan Optimasi Video Streaming Berbasis AI Untuk Pengalaman Penggunaan Multimedia yang Lebih Baik,” J. SAINS STUDENT Res., vol. 3, no. 2, pp. 390–394, 2025, doi: https://doi.org/10.61722/jssr.v3i2.4323.
F. Fitroh, J. Nurhidayah, and Z. Zulfiandri, “Tren dan Tantangan Arsitektur Komputasi Neuromorfik: Tinjauan Literatur Sistematis,” J. Teknol. Sist. Inf., vol. 6, no. 1, pp. 103–113, 2025, doi: https://doi.org/10.35957/jtsi.v6i1.10046.
D. Suryadi, C. S. Octiva, T. I. Fajri, U. W. Nuryanto, and M. L. Hakim, “Optimasi Kinerja Sistem IoT Menggunakan Teknik Edge Computing,” J. Minfo Polgan, vol. 13, no. 2, pp. 1456–1461, 2024, doi: 10.33395/jmp.v13i2.14102.
R. Parapat, “Kompresi Video Digital Menggunakan Metode Embedded Zerotree Wavelet (EZW),” J. Comput. Informatics Res., vol. 1, no. 3, pp. 65–70, 2022, doi: https://doi.org/10.47065/comforch.v1i3.320.
A. Yasir and B. S. Hasugian, “Penggunaan Teknik Kompresi Jpeg Dalam Perancangan Kompresi Citra Digital Memakai Fungsi Gui Pada Matlab,” War. Dharmawangsa, vol. 16, no. 4, pp. 1056–1066, 2022, doi: https://doi.org/10.46576/wdw.v16i4.2454.
I. Syuhada, “Implementasi Algoritma Arithmetic Coding dan Sannon-Fano Pada Kompresi Citra PNG,” TIN Terap. Inform. Nusant., vol. 2, no. 9, pp. 527–532, 2022, doi: https://doi.org/10.47065/tin.v2i9.1027.
O. Irawati, N. Nurhasanah, A. T. Lumbantoruan, and S. F. Zahrani, “Analisis Peforma Algoritma Kompresi Huffman, LZW, BZIP2, Zstandard dan Brotli dalam Efisiensi Penyimpanan Data dan Transfer Data,” RIGGS J. Artif. Intell. Digit. Bus., vol. 4, no. 2, pp. 5163–5169, 2025, doi: https://doi.org/10.31004/riggs.v4i2.1396.
J. L. Phandany, A. M. Sambul, and A. S. M. Lumenta, “Studi Perbandingan Algoritma Kompresi Optimal Citra Digital Menggunakan Python,” J. Tek. Elektro dan Komput., vol. 11, no. 1, pp. 23–34, 2022, doi: https://doi.org/10.35793/jtek.v11i1.37209.
T. Susim and C. Darujati, “Pengolahan citra untuk pengenalan wajah (face recognition) menggunakan OpenCV,” J. Syntax Admiration, vol. 2, no. 3, pp. 534–545, 2021, doi: https://doi.org/10.46799/jsa.v2i3.202.
R. I. Dinata and M. Pratama, “Hubungan antara social comparison dengan body image dewasa awal pengguna media sosial tiktok,” Ranah Res. J. Multidiscip. Res. Dev., vol. 4, no. 3, pp. 217–224, 2022, doi: https://doi.org/10.38035/rrj.v4i3.477.
E. Halawa and S. Purba, “Aplikasi Watermarking Dengan Metode Discrete Cosine Transform (Dct),” J. Sains dan Teknol. ISTP, vol. 18, no. 2, pp. 142–148, 2023, doi: https://doi.org/10.59637/jsti.v18i2.219.
M. S. Moelya, P. S. Ramadhan, and M. G. Suryanata, “Perbandingan Metode Canny, Sobel, Dan Laplacian of Gaussian Dalam Mendeteksi Tepi Citra Objek Bergerak,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 3, no. 4, pp. 450–460, 2024, doi: https://doi.org/10.53513/jursi.v3i4.6466.
B. D. Raharja, “PENERAPAN DISCRETE COSINE TRANSFORM (DCT) TERHADAP KOMPRESI CITRA DIGITAL,” Indones. J. Bus. Intell., vol. 4, no. 1, pp. 31–36, 2021, doi: http://dx.doi.org/10.21927/ijubi.v4i1.1790.
H. Rayra, Y. Yassir, and R. Rachmawati, “Penggunaan Koreksi Gamma Dengan Metode Robert, Prewitt, Dan Sobel Untuk Penyempurnaan Gambar Pada Citra Dalam Air,” J. TEKTRO, vol. 7, no. 1, pp. 65–71, 2023, doi: http://dx.doi.org/10.30811/tektro.v7i1.3863.
H. S. Pal, A. Kumar, A. Vishwakarma, and L. K. Balyan, “A hybrid 2d ecg compression algorithm using dct and embedded zero tree wavelet,” in 2022 IEEE 6th Conference on Information and Communication Technology (CICT), IEEE, 2022, pp. 1–5. doi: 10.1109/CICT56698.2022.9997915.
P. V Bindu and A. Jabeena, “ROI and Non-ROI Image Compression Using Optimal Zero Tree Wavelet and Enhanced Convolutional Neural Network for MRI Images,” SN Comput. Sci., vol. 5, no. 1, pp. 1–9, 2023, doi: https://doi.org/10.1007/s42979-023-02335-6.
P. Padmapriya and V. Rajamani, “A Real?Time Epilepsy Detection Method Using Embedded Zero Tree Wavelet Approach and Support Vector Machine,” Behav. Neurol., vol. 2025, no. 1, pp. 1–21, 2025, doi: https://doi.org/10.1155/bn/5916201.
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