An Algorithm for Detecting Frame Errors Based on RGB Histogram Oscillations in Video Streams
DOI:
https://doi.org/10.37547/ajast/Volume05Issue05-22Keywords:
Frame error detection, RGB histogram, video analysisAbstract
Video frame errors caused by data corruption, compression artifacts, or transmission noise can severely impact visual quality and automated analysis. This paper presents a lightweight and interpretable algorithm for detecting such errors using color histogram analysis. The method constructs and normalizes histograms across RGB channels, identifies the frequency of color oscillations, and classifies frames as normal or erroneous based on a minimal oscillation threshold. Experimental evaluations confirm that the approach is efficient, suitable for real-time applications, and effective in detecting visually corrupted frames.
References
A. Puziy, M. Arabboev, and S. Begmatov, “A STUDY ON SOFTWARE TOOLS FOR DETECTING FRAME ERRORS AND BOUNDARY DISTORTIONS IN VIDEO STREAMS,” Cent. ASIAN J. Acad. Res., vol. 2, no. 10, pp. 42–55, 2024.
A. Puziy, N. Juraeva, K. Davletova, M. Arabboev, and S. Begmatov, “A COMPREHENSIVE REVIEW ON DETECTING FRAME ERRORS IN VIDEO STREAMS THROUGH IMAGE PROCESSING,” Dev. Sci., vol. 1, no. 4, pp. 9–20, 2025.
Y. Xiang, L. Feng, S. Xie, and Z. Zhou, “An efficient spatio-temporal boundary matching algorithm for video error concealment,” Multimed. Tools Appl., vol. 52, no. 1, pp. 91–103, 2011.
C. Nguyen The and D. Shashev, “Methods and Algorithms for Detecting Objects in Video Files,” MATEC Web Conf., vol. 155, pp. 1–6, 2018.
D. A. Gavrilov, “Quality Assessment of Objects Detection and Localization in а Video Stream,” Her. Bauman Moscow State Tech. Univ. Ser. Instrum. Eng., no. 2 (125), pp. 40–55, 2019.
M. Xu, P. Fu, B. Liu, and J. Li, “Multi-Stream Attention-Aware Graph Convolution Network for Video Salient Object Detection,” IEEE Trans. Image Process., vol. 30, pp. 4183–4197, 2021.
Z. Ameur, S. A. Fezza, and W. Hamidouche, “Deep multi-task learning for image/video distortions identification,” Neural Comput. Appl., vol. 34, no. 24, pp. 21607–21623, 2022.
J. Gowri Shankar, A. S. Kumar, R. Sekaran, M. Parasuraman, S. Annamalai, and T. Narmadha, “Detection of Objects in High-Definition Videos for Disaster Management,” 2023 Int. Conf. Comput. Sci. Emerg. Technol. CSET 2023, pp. 1–6, 2023.
Y. Yang, Z. Fu, and S. M. Naqvi, “Abnormal event detection for video surveillance using an enhanced two-stream fusion method,” Neurocomputing, vol. 553, no. July, p. 126561, 2023.
J. Huizhen, Z. Huaibo, Q. Hongzheng, and W. Tonghan, “Dual-stream mutually adaptive quality assessment for authentic distortion image,” J. Vis. Commun. Image Represent., vol. 102, no. June, p. 104216, 2024.
Y. Zhang, H. Lin, J. Sun, L. Zhu, and S. Kwong, “Learning to Predict Object-Wise Just Recognizable Distortion for Image and Video Compression,” IEEE Trans. Multimed., vol. 26, pp. 5925–5938, 2024.
J. B. Du, G. P. Mayuga, and M. L. Guico, “Performance evaluation and integration of distortion mitigation methods for fisheye video object detection,” Int. J. Adv. Appl. Sci., vol. 13, no. 3, pp. 743–758, 2024.
O. Laktionov and A. Yanko, “DEVELOPMENT OF A HARDWARE- SOFTWARE SOLUTION FOR DETECTION OF COMPLEX-SHAPED OBJECTS,” Technol. Audit Prod. Reserv., vol. 2, no. 80, pp. 35–40, 2024.
W. Voravuthikunchai, B. Crémilleux, and F. Jurie, “Histograms of pattern sets for image classification and object recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 224–231, 2014.
C. Vilar, S. Krug, and B. Thörnberg, “Processing chain for 3D histogram of gradients based real-time object recognition,” Int. J. Adv. Robot. Syst., vol. 18, no. 1, pp. 1–13, 2021.
P. Siva, M. J. Shafiee, M. Jamieson, and A. Wong, “Real-Time, Embedded Scene Invariant Crowd Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG),” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 885–892, 2016.
S. Majumdar and K. S. Rao, “A Color Histogram-Based Approach for Scene Segmentation in a Video,” 2024 IEEE Int. Conf. Smart Power Control Renew. Energy, ICSPCRE 2024, pp. 1–5, 2024.
R. Hannane, A. Elboushaki, and K. Afdel, “Efficient Video Summarization Based on Motion SIFT-Distribution Histogram,” Proc. - Comput. Graph. Imaging Vis. New Tech. Trends, CGiV 2016, pp. 312–317, 2016.
T. Hu and Z. Li, “Video summarization via exploring the global and local importance,” Multimed. Tools Appl., vol. 77, no. 17, pp. 22083–22098, 2018.
H. M-PATEL, T. SHARMA, and N. PANDYA, “VIDEO SUMMARIZATION USING UNSUPERVISED LEARNING USING COLOR HISTOGRAM,” Int. J. Electr. Electron. Data Commun., vol. 6, no. 5, pp. 13–17, 2018.
B. Liang, N. Li, Z. He, Z. Wang, Y. Fu, and T. Lu, “News video summarization combining surf and color histogram features,” Entropy, vol. 23, no. 8, 2021.
B. U. Gadhia and S. S. Modasiya, “A Comparative analysis of Video Summarization techniques for different domains,” SAMRIDDHI A J. Phys. Sci. Eng. Technol., vol. 15, no. 02, pp. 253–257, 2023.
T. Alaa, A. Mongy, A. Bakr, M. Diab, and W. Gomaa, “Video Summarization Techniques: A Comprehensive Review,” Proc. Int. Conf. Informatics Control. Autom. Robot., vol. 2, no. Icinco 2024, pp. 141–148, 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anastasiya Puziy, Mukhriddin Arabboev, Shohruh Begmatov, Ruxshona Nabiyeva, Kholisakhon Davletova
This work is licensed under a Creative Commons Attribution 4.0 International License.
