Uji Efektifitas Kompresi Golomb-Rice dan Huffman untuk Metadata EXIF dalam File JPEG
DOI:
https://doi.org/10.14421/jiska.5060Keywords:
Golomb-Rice, Huffman, EXIF, JPEG, Compression RatioAbstract
Compression algorithms are now called modern compression algorithms. This improvement is characterized by the combination of various classical techniques and is even based on machine learning and AI. However, the important part of compression is not only the algorithm, but also knowledge of the internal structure and metadata of the file is required. Like JPEG has a file structure that can be changed, cannot be changed, every marker (header), and EXIF metadata. Lack of knowledge of the file structure can cause data damage and file corruption. This study evaluates the compression of EXIF metadata of JPEG files using the Golomb-Rice and Huffman algorithms. Golomb-Rice can produce compression that affects the k parameter, while Huffman is optimal based on symbol frequency, but requires a code table. This study measures the effectiveness of both algorithms based on the compression ratio (CR). The test results of Golomb-Rice are more effective than those of Huffman. So, it can be concluded that the Golomb-Rice algorithm is superior in the context of compressing EXIF JPEG metadata, while Huffman shows lower efficiency in the tested scenarios.
References
Acharya R, S., J, S., S, S., S Aithal, V., & G S, H. (2023). Geo-Locating an Image Using EXIF Data. International Journal of Engineering Applied Sciences and Technology, 8(1), 43–47. https://doi.org/10.33564/IJEAST.2023.v08i01.007
Agnihotri, S., Rameshan, R. M., Ghosal, R., & Gupta, P. (2024). Lossless Binary Image Compression Using Learned Multi-Level Dictionaries. 2024 60th Annual Allerton Conference on Communication, Control, and Computing, 1–8. https://doi.org/10.1109/Allerton63246.2024.10735272
Ardiansyah, A., Hardi, N., & Gata, W. (2020). Identifikasi dan Recovery File JPEG dengan Metode Signature-Based Carving dalam Model Automata. Komputika: Jurnal Sistem Komputer, 9(1), 75–83. https://doi.org/10.34010/komputika.v9i1.2733
Aria, M., & Sanjaya, A. (2018). Teknik Kompresi pada Transmisi Data Citra Payload KOMURINDO. Komputika : Jurnal Sistem Komputer, 7(2), 103–111. https://doi.org/10.34010/komputika.v7i2.1512
Azeem, E. A., & Fatima. (2022). The Data Carving - The Art of Retrieving Deleted Data as Evidence. International Journal for Electronic Crime Investigation, 6(2), 8. https://doi.org/10.54692/ijeci.2022.0602101
Boddu, M., & Mandal, S. K. (2022). Quantum-dot Cellular Automata Based Lossless CFA Image Compression Using Improved and Extended Golomb-rice Entropy Coder. International Journal of Intelligent Engineering and Systems, 15(2), 12–25. https://doi.org/10.22266/ijies2022.0430.02
Cai, S., Liang, X., Cao, S., Yan, L., Zhong, S., Chen, L., & Zou, X. (2024). Powerful Lossy Compression for Noisy Images. 2024 IEEE International Conference on Multimedia and Expo (ICME), 1–6. https://doi.org/10.1109/ICME57554.2024.10687468
Cappello, F., Acosta, M., Agullo, E., Anzt, H., Calhoun, J., Di, S., Giraud, L., Grützmacher, T., Jin, S., Sano, K., Sato, K., Singh, A., Tao, D., Tian, J., Ueno, T., Underwood, R., Vivien, F., Yepes, X., Kazutomo, Y., & Zhang, B. (2025). Multifacets of Lossy Compression for Scientific Data in the Joint-Laboratory of Extreme Scale Computing. Future Generation Computer Systems, 163, 107323. https://doi.org/10.1016/j.future.2024.05.022
Dhawan, S. (2011). A Review of Image Compression and Comparison of Its Algorithms. IJECT, 2(1). https://www.iject.org/vol2issue1/sachindhawan.pdf
Doménech Fons, J., & Pegueroles Vallés, J. R. (2021). Study and Development of an Autopsy Module for Automated Analysis of Image Metadata [Universitat Politècnica de Catalunya]. https://hdl.handle.net/2117/356815
Hamilton, A., El-Hajjar, M., & Maunder, R. G. (2023). Reordered Exponential Golomb Error Correction Code for Universal Near-Capacity Joint Source-Channel Coding. IEEE Access, 11, 93619–93634. https://doi.org/10.1109/ACCESS.2023.3310824
Harvey, P. (2025). JPEG Tags. In EXIFTool. https://EXIFtool.org/TagNames/JPEG.html
Hazel, T. (2008, September). JPEG Marker Codes. TechStumbler. https://techstumbler.blogspot.com/2008/09/jpeg-marker-codes.html
Himalyan, S., & Gupta, V. (2023). Golomb–Rice Coder-Based Hybrid Electrocardiogram Compression System. ECSA 2023, 2023, Article ID: 10. https://doi.org/10.3390/ecsa-10-16209
Hwang, I., Yun, J., Chung, W., Lee, J., Kim, C.-G., Kim, Y., & Park, W.-C. (2021). Lossless Compression Algorithm and Architecture for Reduced Memory Bandwidth Requirement with Improved Prediction Based on the Multiple DPCM Golomb-Rice Algorithm. Journal of Web Engineering, 20(6), 1813–1828. https://doi.org/10.13052/jwe1540-9589.2065
Itier, V., Puteaux, P., & Puech, W. (2022). Image Crypto‐Compression. In M. Security (Ed.), Multimedia Security 2 (pp. 91–128). Wiley. https://doi.org/10.1002/9781119987390.ch4
Jamil, S. (2024). Review of Image Quality Assessment Methods for Compressed Images. Journal of Imaging, 10(5), Article ID: 113. https://doi.org/10.3390/jimaging10050113
Kadhim, D. J., Mosleh, M. F., & Abed, F. A. (2024). Exploring Text Data Compression: A Comparative Study of Adaptive Huffman and LZW Approaches. BIO Web of Conferences, 97, Article ID: 00035. https://doi.org/10.1051/bioconf/20249700035
Kumar, G., & Kumar, R. (2021). Analysis of Arithmetic and Huffman Compression Techniques by Using DWT-DCT. International Journal of Image, Graphics and Signal Processing, 13(4), 63–70. https://doi.org/10.5815/ijigsp.2021.04.05
Liu, X., An, P., Chen, Y., & Huang, X. (2022). An Improved Lossless Image Compression Algorithm Based on Huffman Coding. Multimedia Tools and Applications, 81(4), 4781–4795. https://doi.org/10.1007/s11042-021-11017-5
Mahmood, A., & Wagner, A. B. (2023). Lossy Compression with Universal Distortion. IEEE Transactions on Information Theory, 69(6), 3552–3573. https://doi.org/10.1109/TIT.2023.3247601
Mani, R. G., Parthasarathy, R., Eswaran, S., & Honnavalli, P. (2022). A Survey on Digital Image Forensics: Metadata and Image Forgeries. WAC-2022: Workshop on Applied Computing, 22. https://ceur-ws.org/Vol-3142/PAPER_03.pdf
Martini, M. G. (2025). Measuring Objective Image and Video Quality: On the Relationship Between SSIM and PSNR for DCT-based Compressed Images. IEEE Transactions on Instrumentation and Measurement, 74, 1–13. https://doi.org/10.1109/TIM.2025.3529045
Mills, R. (2018). The Metadata in JPEG Files. https://dev.exiv2.org/projects/exiv2/wiki/The_Metadata_in_JPEG_files
Nousheen, M. S., & Kumar, T. V. (2023). Implementation of Lossless Image Compression Using Fuzzy Based Modified Golomb Rice Encoding. Journal of Engineering Sciences, 14(02), 242–253. https://doi.org/10.15433.JES.2023.V14I2.43P.29
Stoilov, E. P. (2022). Discovery and Analysis of EXIF Data in Images. 61 St Annual Scientific Conference - University of Ruse and Union of Scientists, 52–58. https://conf.uni-ruse.bg/bg/docs/cp22/bp/bp-6.pdf
Sunardi, S., Riadi, I., & Akbar, M. H. (2020). Steganalisis Bukti Digital pada Media Penyimpanan Menggunakan Metode Static Forensics. Jurnal Nasional Teknologi dan Sistem Informasi, 6(1), 1–8. https://doi.org/10.25077/TEKNOSI.v6i1.2020.1-8
Taha, H., Luisa, S., Nasir, V., & Editors, M. (2022). Multimedia Forensics (H. T. Sencar, L. Verdoliva, & N. Memon, Eds.). Springer Singapore. https://doi.org/10.1007/978-981-16-7621-5
Wijayanto, H., Riadi, I., & Prayudi, Y. (2016). Encryption EXIF Metadata for Protection Photographic Image of Copyright Piracy. IJRCCT, 5(5), 237–243.
Žalik, B., Mongus, D., Žalik, K. R., Podgorelec, D., & Lukač, N. (2021). Lossless Chain Code Compression with an Improved Binary Adaptive Sequential Coding of Zero-Runs. Journal of Visual Communication and Image Representation, 75, Article ID: 103050. https://doi.org/10.1016/j.jvcir.2021.103050
Zheng, C., Shrivastava, A., & Owens, A. (2023). EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6945–6956. https://doi.org/10.1109/CVPR52729.2023.00671
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