Analisis Komparatif Metode Interpolasi Bicubic dan Lanczos4 Untuk Peningkatan Resolusi Citra Plat Nomor Kendaraan pada CCTV Publik Kota Yogyakarta
DOI:
https://doi.org/10.35746/jtim.v8i3.1097Kata Kunci:
CCTV, License Plate, Interpolation, PSNR, SSIMAbstrak
Public CCTV image quality in Yogyakarta's tourism areas often suffers from low resolution, complicating automatic vehicle license plate identification. This study contributes an empirical comparative evaluation of classic interpolation methods (Bicubic Interpolation and Lanczos4 In-terpolation) applied to real-world public CCTV traffic scenarios. High-Resolution (HR) reference images were downsampled by 4x to simulate low-resolution (LR) CCTV footage, then upscaled back using both methods. Performance was quantitatively measured using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Experimental results on 10 sample images show that Bicubic Interpolation achieves a slightly higher average performance (PSNR 19.47 dB and SSIM 0.5325) compared to Lanczos4 (PSNR 19.25 dB and SSIM 0.5128). However, qualitative visual analysis confirms that Lanczos4 produces sharper character edges. Nevertheless, as this qualitative enhancement has not been quantitatively validated through Optical Character Recognition (OCR) testing or statistical analysis, these conventional interpolation methods serve pri-marily as preliminary preprocessing steps for potential integration into automatic recognition systems.
Unduhan
Referensi
M. Hijji, A. Khan, M. M. Alwakeel, R. Harrabi, F. Aradah, F. A. Cheikh, M. Sajjad, and K. Muhammad, "Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications," Mathematics, vol. 11, no. 4, hlm. 892, 2023. https://doi.org/10.3390/math11040892
Y.-W. Lee and B.-G. Kim, "Stroke-Aware Flow for License Plate Recognition," Journal of Multimedia Information System, vol. 13, no. 1, hlm. 13-20, 2026. https://doi.org/10.33851/JMIS.2026.13.1.13
V. Nascimento, G. E. Lima, R. O. Ribeiro, W. R. Schwartz, R. Laroca, and D. Menotti, "Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark," Journal of the Brazilian Computer Society, vol. 31, no. 1, hlm. 435–448, 2025. https://doi.org/10.5753/jbcs.2025.5159
C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, hlm. 295-307, 2016. https://doi.org/10.1109/TPAMI.2015.2439281
Ş. Sağlam and A. Çelik, "Solution Proposal for Accelerating Interpolation Algorithms in Image Scaling: A Comparative Application," ESTUDAM Bilişim Dergisi, vol. 1, no. 1, hlm. 24-30, 2026. https://doi.org/10.53608/estudambilisim.1844275
T. Tinaliah and T. Elizabeth, "Analisis Hasil Resolusi Citra Dengan Metode Interpolasi Nearest Neighbor, Interpolasi Bi-linear, dan Interpolasi Bicubic," JuSiTik: Jurnal Sistem dan Teknologi Informasi Komunikasi, vol. 2, no. 1, hlm. -, 2022. https://doi.org/10.32524/jusitik.v2i1.428
L. Orellano-Benancio, R. Muñoz-Canales, D. Humpire-Molina, and E. L. Huamaní, "Identification of alphanumeric char-acters in license plates based on image processing with commercial software as a complement or alternative to image processing with Amped FIVE forensic software," International Journal of Emerging Trends in Engineering Research, vol. 8, no. 5, hlm. 4920-4926, 2020. https://doi.org/10.30534/ijeter/2020/05892020
N. Cahyadi, S. Salsabila, S. Raniprima, and V. Monita, "Tinjauan Literatur Teknik Pengenalan Plat Nomor Berbasis Deep Learning," Jurnal Teknologika, vol. 14, no. 2, hlm. 458-469, 2024. https://doi.org/10.51132/teknologika.v14/2
S. Sugeng and N. Widyanto, "Perbaikan Kualitas Gambar untuk Deteksi Plat Nomor Kendaraan dengan Metode Super Resolution GANs," Jurnal Buana Informatika, vol. 16, no. 2, hlm. -, 2025. https://doi.org/10.24002/jbi.v16i2.11483
V. Nascimento, R. Laroca, J. A. Lambert, W. R. Schwartz, and D. Menotti, "Super-resolution of license plate images using attention modules and sub-pixel convolution layers," Computers & Graphics, vol. 113, no. -, hlm. 69-76, 2023. https://doi.org/10.1016/j.cag.2023.05.005
Y. Pan, J. Tang, and T. Tjahjadi, "LPSRGAN: Generative adversarial networks for super-resolution of license plate image," Neurocomputing, vol. 580, no. -, hlm. 127426, 2024. https://doi.org/10.1016/j.neucom.2024.127426
M. Lin, L. Liu, F. Wang, J. Li, and J. Pan, "License plate image reconstruction based on generative adversarial networks," Remote Sensing, vol. 13, no. 15, hlm. 3018, 2021. https://doi.org/10.3390/rs13153018.
A.Hamdi, Y. K. Chan, and V. C. Koo, "A new image enhancement and super resolution technique for license plate recogni-tion," Heliyon, vol. 7, no. 11, hlm. e08341, 2021. https://doi.org/10.1016/j.heliyon.2021.e08341
K. Mehrgan, A. R. Ahmadyfard, and H. Khosravi, "Super-resolution of License-plates Using Weighted Interpolation of Neighboring Pixels from Video Frames," International Journal of Engineering, TRANSACTIONS B: Applications, vol. 33, no. 5, hlm. 992-999, 2020. https://doi.org/10.5829/ije.2020.33.05b.33.
S. Pan, S.-B. Chen, and B. Luo, "A super-resolution-based license plate recognition method for remote surveillance," Journal of Visual Communication and Image Representation, vol. 94, no. -, hlm. 103844, 2023. https://doi.org/10.1016/j.jvcir.2023.103844
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Rifai Ahmad Musthofa, Imam Ainudin Pirmansah, Dhimas Adi Satria
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
