2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 2 , PP: 249-263, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Iptehaj Alhakam 1 , Ali Abdullah Ali 2 , Oday Ali Hassen 3 * , Saad M. Darwish 4 , Nur Azman Abu 5
Doi: https://doi.org/10.54216/FPA.170219
Solving the video compression problem requires a multi-faceted approach, balancing quality, efficiency, and computational demands. By leveraging advancements in technology and adapting to the evolving needs of video applications, it is possible to develop compression methods that meet the challenges of the present and future digital landscape. To address these objectives, machine learning and AI approaches can be utilized to predict and remove redundancies more effectively, optimizing compression algorithms dynamically based on content. Still, state-of-the art neural network-based video compression models need large and diverse datasets to generalize well across different types of video content. Wavelets can provide both time (spatial) and frequency localization, making them highly effective for video compression. This dual localization allows wavelet transforms to handle both rapid changes in video content and slow-moving scenes efficiently, leading to better compression ratios. Yet, some wavelet coefficients may be more critical for maintaining visual quality than others. Inaccurate quantization can lead to noticeable degradation. For the first time, the suggested model combine Quantum Wavelet Transform (QWT) and Neural Networks (NN) for video compression. This fusion model aims to achieve higher compression ratios, maintain video quality, and reduce computational complexity by utilizing QWT’s efficient data representation and NN’s powerful pattern recognition and predictive capabilities. Quantum bits (qubits) can encode large amounts of information in their quantum states, enabling more efficient data representation. This is especially useful for encoding large video files. Furthermore, quantum entanglement allows for correlated data representation across qubits, which can be exploited to capture intricate details and redundancies in video data more effectively than classical methods. The experimental results reveal that QWT achieves a compression ratio of almost twice that of traditional WT for the same video, maintaining superior visual quality due to more efficient redundancy elimination.
Video compression , Quantum wavelet transform , Neural network , Adaptive coding , Optimization
[1] Zhang, Y., Zhu, L., Jiang, G., Kwong, S., Kuo, C. A survey on perceptually optimized video coding. ACM Computing Surveys, 55(12):1-37, 2023.
[2] Bidwe, R., Mishra, S., Patil, S., Shaw, K., Vora, D., Kotecha, K., Zope, B. Deep learning approaches for video compression: a bibliometric analysis. Big Data and Cognitive Computing, 6(2):44, pp.1-40, 2022.
[4] Yang, J., Yang, C., Zhai, Y., Wang, Q., Pan, X., Wang, R. Improving learned video compression by exploring spatial redundancy. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2860-2864, 2024.
[5] Gowrisetty, V., Fernando, A. Static video compression’s influence on neural network performance. Electronics, 12(1):1-23, 8, 2022.
[6] Gunanandhini, S., Kalamani, M., Bhagavathipriya, M. Wavelet based Video Compression techniques for Industrial monitoring applications. Journal of Physics: Conference Series, 2272(1):1-8, 012019, IOP Publishing, 2022.
[7] Nithin, S., Suresh, L., Krishnaveni, S., Muthukumar, P. Developing novel video coding model using modified dual-tree wavelet-based multi-resolution technique. Multimedia Systems, 28(2):643-657, 2022.
[8] Bagherimehrab, M., Aspuru-Guzik, A. Efficient quantum algorithm for all quantum wavelet transforms. Quantum Science and Technology, 9(3):1-16, 035010, 2024.
[9] Mu, X., Wang, H., Bao, R., Wang, S., Ma, H. An improved quantum watermarking using quantum Haar wavelet transform and Qsobel edge detection. Quantum Information Processing, 22(5):1-16, 223, 2023.
[10] Zeng, H., Xu, J., He, S., Deng, Z., Shi, C. Rate Control Technology for Next Generation Video Coding Overview and Future Perspective. Electronics, 11(23):1-25, 4052, 2022.
[11] Choi, K. A study on fast and low-complexity algorithms for versatile video coding. Sensors, 22(22):8990, 2022.
[12] Chen, L., Cheng, B., Zhu, H., Qin, H., Deng, L., Luo, L. Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications. Electronics, 13(11):2150, 2024.
[13] Joy, H., Kounte, M. Deep CNN Based Interpolation Filter for High Efficiency Video Coding. In Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things, pp. 519-524, 2024.
[14] Lee, M., Park, S., Oh, S., Kim, Y., Jeong, S., Lee, J., Sim, D. Transform-Based Feature Map Compression Method for Video Coding for Machines (VCM). Electronics, 12(19):4042, 2023.
[15] Hu, Y., Jung, C., Qin, Q., Han, J., Liu, Y., Li, M. HDVC: Deep Video Compression with Hyperprior-Based Entropy Coding, IEEE Access, 2024.
[16] Chen, S., Aramvith, S., Miyanaga, Y. Learning-Based Rate Control for High Efficiency Video Coding. Sensors, 23(7):3607, 2023.
[17] Zhao, Y., He, W., Jia, C., Wang, Q., Li, J., Li, Y., Lin, C., Zhang, K., Zhang, L., Ma, S. A Neural-network Enhanced Video Coding Framework beyond ECM. arXiv preprint arXiv:2402.08397, 2024.
[18] Zhang, Y., Gong, X., Yu, H., Wu, Z., Yu, L. Distance-based feature repack algorithm for video coding for machines. Journal of Visual Communication and Image Representation, 16:104150, 2024.
[19] Thanh, H., Quang, S., Huu, T., Hoang, X. Learning adaptive motion search for fast versatile video coding in visual surveillance systems. IET Image Processing, 8(4):981-95, 2024.
[20] Bian, Y., Sheng, X., Li, L., Liu, D. LSSVC: A Learned Spatially Scalable Video Coding Scheme. IEEE Transactions on Image Processing, 33(3): 3314 – 3327, 2024.
[21] Zouidi, N., Kessentini, A., Hamidouche, W., Masmoudi, N., Menard, D. Multitask learning based intra-mode decision framework for versatile video coding. Electronics, 11(23):4001, 2022.
[22] Yang, C., Qin, S., An, P., Huang, X., Shen, L. Content adaptive spatial-temporal rescaling for video coding optimization. Expert Systems with Applications, 124482, 2024.
[23] Samarathunga, B., Ganearachchi, Y., Fernando, T., Alahapperuma, I., Fernando, A. Semantic Communication Based Video Coding Using Temporal Prediction of Deep Neural Network Parameters. In Proceedings of the International Conference on Gaming, Entertainment and Media (GEM) Conference, IEEE, ITA, 2024.
[24] Ibraheem, M., Dvorkovich, A. Enhancing Versatile Video Coding Efficiency via Post-Processing of Decoded Frames Using Residual Network Integration in Deep Convolutional Neural Networks. In Proceedings of the International Conference on Digital Signal Processing and its Applications, pp. 1-9, IEEE, 2024.
[25] Qin, S., Yang, C., An, P. Content adaptive downsampling for low bitrate video coding. Multimedia Tools and Applications, 83(9):26547-63, 2024.
[26] Reich, C., Debnath, B., Patel, D., Prangemeier, T., Cremers, D., Chakradhar, S. Deep Video Codec Control for Vision Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5732-5741, 2024.
[27] Jiang, X., Xiang, M., Jin, J., Song, T. Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding. Information, 14(9):494, 2023.
[28] Sheng, X., Li, L., Liu, D., Li, H. VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7): 4579 – 4596, 2024.
[29] Lee, D., Kwon, S. Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning. Sensors, 22(24):9656, 2022.
[30] Sharrab, Y., Alsmirat, M., Eljinini, M., Sarhan, N. iHELP: a model for instant learning of video coding in VR/AR real-time applications. Multimedia Tools and Applications, 1-40, 2024.
[31] Bender, I., Rehbein, G., Correa, G., Agostini, L., Porto, M. Adaptive complexity control for AV1 video encoder using machine learning. Journal of Real-Time Image Processing, 21(3):96, 2024.
[32] Delibaşoğlu, İ. Moving object detection method with motion regions tracking in background subtraction. Signal, Image and Video Processing, 7(5):2415-23, 2023.
[33] Ammous, D., Kessentini, A., Khlif, N., Kammoun, F., Masmoudi, N. An Enhancement of Lossless Video Compression Using Two-Layer Approach. In Applications of Encryption and Watermarking for Information Security, pp. 105-135, IGI Global, 2023.
[34] Zhu, L., Zhang, Y., Li, N., Wu, W., Wang, S., Kwong, S. Neural Network Based Multi-Level In-Loop Filtering for Versatile Video Coding. IEEE Transactions on Circuits and Systems for Video Technology. 2024 Jun 28.
[35] Nithin, S., Suresh, L., Krishnaveni, S., Muthukumar, P. Developing novel video coding model using modified dual-tree wavelet-based multi-resolution technique. Multimedia Systems, 28(2):643-657, 2022.
