Volume 15 , Issue 2 , PP: 43-64, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Oday Ali Hassen 1 * , Huda Lafta Majeed 2 , Mohammed Abdulhasan Hussein 3 , Saad M. Darwish 4 , Omar Al-Boridi 5
Doi: https://doi.org/10.54216/JCIM.150205
The transmission of video is greatly aided by video compression. Redundancy (spatial, temporal, statistical, and psycho-visual) within and between video frames is something that video compression approaches aim to get rid of. The degree to which similarity-based redundancy exists between consecutive frames, however, is a function of how often the frames are sampled and how the objects in the scene are moving. Existing neural network-based video compression approaches rely on a static codebook to perform compression, which prevents them from adapting to new video’s data. In order to create an optimal codebook for vector quantization, which is then employed as an activation function inside a neural network's hidden layer, this research offers a modified video compression method based on a Qutrits based Quantum Genetic Algorithm (QQGA). Using quantum parallelization and entanglement of the quantum state, QQGA is capable of solving the same set of problems as a traditional genetic algorithm while considerably accelerating the evolutionary process. The technique is built on the concept of utilizing Qutrits (three-level quantum system) to represent population individuals. The evolution operator, which is responsible for the updates to the quantum system state, has been constructed using a straightforward approach that does not need a lookup table. Compared to qubit, qudit provides a larger state space to store and process information, and thus can enhance the algorithm’s efficiency. To create the context-based initial codebook, the background subtraction algorithm is used to extract moving objects from frames. Moreover, important wavelet coefficients are compressed losslessly using Differential Pulse Code Modulation (DPCM), whereas low energy coefficients are compressed lossy using Learning Vector Quantization neural networks (LVQ). To obtain a high compression ratio, Run-Length Encoding is then used to encode the quantized coefficients. In comparison to the conventional evolutionary algorithm-based video compression method, experiments have shown that the quantum-inspired system may achieve a greater compression ratio with acceptable efficiency as evaluated by PSNR.
Video compression , Adaptive coding , Optimization , Quantum machine learning , Context-based compression, Quantum genetic algorithm
[1] Zhang, Y., Zhu, L., Jiang, G., Kwong, S., Kuo, C. A survey on perceptually optimized video coding. ACM Computing Surveys, vol. 55, no. 12, pp. 1-37, 2023.
[2] Ji, R., Karam, L. Learning-based Visual Compression. Foundations and Trends in Computer Graphics and Vision, vol.15, no. 1, pp. 1-12, 2023.
[3] Dong, Y., Pan, W. A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques. Information, vol.14, no. 3,184, pp.1-21, 2023.
[4] Aliouat, A., Kouadria, N., Maimour, M., Harize, S., Doghmane, N. Region-of-interest based video coding strategy for rate/energy-constrained smart surveillance systems using WMSNs. Ad Hoc Networks, vol. 140, 103076, pp.1-13, 2023.
[5] Jeny, A., Islam, M. Optimized video compression with residual split attention and swin-block artifact contraction. Journal of Visual Communication and Image Representation, vol. 90, 103737, pp.1-14, 2023.
[6] Gandam, A., Sidhu, J., Singh, M., Kaur, H. Graph Theory-Based HEVC Video Compression of Satellite Videos. In Proceedings of the International Conference on Small Satellites, pp. 23-29, Singapore: Springer Nature Singapore, 2023.
[7] Yang, R., Timofte, R., Li, X., Zhang, Q., Zhang, L., Liu, F., He, D., Li, F., Zheng, H., Yuan, W., Ostyakov, P. Aim 2022 challenge on super-resolution of compressed image and video: Dataset, methods and results.
In Proceedings of the Computer Vision–Workshops, Part III 2023, pp. 174-202, Cham: Springer Nature Switzerland, 2023.
[7] Khadir, M., Hashmi, M. High Efficient Quality of Video Compression using Variational Autoencoders in Deep Learning. In Proceedings of the International Conference on Power, Control & Embedded Systems, pp. 1-6, 2023.
[8] Mozhaeva, A., Mazin, V., Cree, M., Streeter, L. Video quality assessment considering the features of the human visual system. In Proceedings of the International conference on Image and Vision Computing, pp. 288-300, Cham: Springer Nature Switzerland, 2023.
[9] Winkler, S. Perceptual video quality metrics—A review. Digital video image quality and perceptual coding, vol. 19, pp. 155-180, 2017.
[10] Rahebi, J. Vector quantization using whale optimization algorithm for digital image compression. Multimedia Tools and Applications. vol. 81, issue 14, pp. 20077-20103, 2022.
[11] Barman, D., Hasnat, A., Barman, B. A codebook modification method of vector quantization to enhance compression ratio. In Proceedings of the International Conference in High Performance Computing and Networking, pp. 227-234, Singapore: Springer Singapore, 2022.
[12] Barman, D., Hasnat, A., Barman, B. A quantization based codebook formation method of vector quantization algorithm to improve the compression ratio while preserving the visual quality of the decompressed image. Multidimensional Systems and Signal Processing, vol. 34, pp. 127–145, 2023.
[13] Guo, J., Wu, C., Huang, Z., Wang, F., Huang, M. Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, pp. 174-184, Springer International Publishing, 2022.
[14] 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, vol. 6, issue 2, 44, pp. 1-40, 2022.
[15] Hsu, C., Hung, T., Hsu, C. Optimizing immersive video coding configurations using deep learning: A case study on TMIV. ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, issue 1, pp. 1-25, 2022.
[16] Putra, A., Gaffar, A., Sumadi, M., Setiawati, L. Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN). International Journal on Informatics Visualization, vol.6, no. 3, pp. 650-659, 2022.
[17] Moussafir, M., Chaibi, H., Saadane, R., Chehri, A., Rharras, A., Jeon, G. Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks. Plant and Soil, vol. 479, no. 1-2, pp. 251-266, 2022.
[18] Bas, E., Egrioglu, E., Kolemen, E. Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization. Granular Computing, vol. 7, no. 2, pp. 411-420, 2022.
[19] Dubey, R., Agrawal, J. An Improved Genetic Algorithm for Automated Convolutional Neural Network Design. Intelligent Automation & Soft Computing, vol. 32, no. 2, pp. 747-763, 2022.
[20] Chavan, P., Sheela, B., Murugan, M., Chavan, P., Kulkarni, M. An Analysis of Codebook Optimization for Image Compression: Modified Genetic Algorithm and Particle Swarm Optimization Algorithm. In Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems, pp. 849-866, Singapore: Springer Nature Singapore, 2023.
[21] Lamata, L. Quantum machine learning and quantum Biomimetics: A perspective. Machine Learning: Science and Technology, vol. 1, no. 3, 033002, pp. 1-12, 2020.
[22] Zeguendry, A., Jarir, Z., Quafafou, M. Quantum Machine Learning: A Review and Case Studies. Entropy, vol. 25, no. 2, 287, pp.1-41, 2023.
[23] Choe, I., Kim, G., Kim, N., Ko, M., Ryom, J., Han, R., Han, T., Han I. Can quantum genetic algorithm really improve quantum back propagation neural network. Quantum Information Processing, vol. 22, no. 3, 154, pp. 1-12, 2023.
[24] Belkebir, D. DIP-QGA: a secure and robust watermarking technique based on direct image projection and quantum genetic algorithm. International Journal of Information and Computer Security, vol. 20, no. 3-4, pp. 221-247, 2023.
[25] Tkachuk, V. Quantum genetic algorithm based on Qutrits and its application. Mathematical Problems in Engineering, vol.2018, 8614073, pp. 1-8, 2018.
[26] Dutta, T., Dey, S., Bhattacharyya, S., Mukhopadhyay, S., Chakrabarti, P. Hyperspectral multi-level image thresholding using Qutrit genetic algorithm. Expert Systems with Applications, vol. 181, 115107, pp. 1-19, 2021.
[27] Joy, H., Kounte, M., Chandrasekhar, A., Paul, M. Deep Learning Based Video Compression Techniques with Future Research Issues. Wireless Personal Communications, vol.131, pp. 1-27. 2023.
[28] Petreski, D., Kartalov, T. Next Generation Video Compression Standards–Performance Overview. In Proceedings of the 30th International Conference on Systems, Signals and Image Processing, Ohrid, North Macedonia, pp. 1-5, 2023.
[29] Li, J., Li, B., Lu, Y. Neural Video Compression with Diverse Contexts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22616-22626, 2023.
[30] Gomes, C., Azevedo, R., Schroers, C. Video Compression with Entropy-Constrained Neural Representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18497-18506, 2023.
[31] Duong, L., Li, B., Chen, C., Han, J. Multi-rate adaptive transform coding for video compression. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1-5, 2023.
[32] Isik, B., Guleryuz, O., Tang, D., Taylor, J., Chou, P. Sandwiched video compression: Efficiently extending the reach of standard codecs with neural wrappers. ArXiv preprint arXiv: 2303.11473, pp.1-5, 2023.
[33] Jin, D., Lei, J., Peng, B., Pan, Z., Li, L., Ling, N. Learned Video Compression with Efficient Temporal Context Learning. IEEE Transactions on Image Processing, vol. 32, pp. 3188 – 3198, 2023.
[34] Lin, R., Wang, M., Zhang, P., Wang, S., Kwong, S. Multiple Hypotheses Based Motion Compensation for Learned Video Compression. Neurocomputing, vol. 548,126396, pp.1-15, 2023.
[35] Wang, Y., Chan, P., Donzella, V. Semantic-Aware Video Compression for Automotive Cameras. IEEE Transactions on Intelligent Vehicles, vol.8, issue 6, pp.1-12, 2023.
[36] Liu, B., Chen, Y., Machineni, R., Liu, S., Kim, H. MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18487-18496, 2023.
[37] Habib, S., Albattah, W., Alsharekh, M., Islam, M., Shees, M., Sherazi, H. Computer Network Redundancy Reduction Using Video Compression. Symmetry. vol.15, issue 6, 1280, pp.1-14, 2023.
[38] Saini, D., Kamble, S., Shankar, R., Kumar, M., Kapila, D., Tripathi, D. Fractal video compression for IOT-based smart cities applications using motion vector estimation. Measurement: Sensors. vol. 26, 100698, pp.1-15, 2023.
[39] Malani, R., Suprapty, B., Putra, A., Gaffar, A. Inter-frame video compression based on adaptive fuzzy inference system compression of multiple frame characteristics. Knowledge Engineering and Data Science, vol. 6, no. 1, pp. 1-14, 2023.
[40] Pourreza, R., Le, H., Said, A., Sautiere, G., Wiggers, A. Boosting neural video codecs by exploiting hierarchical redundancy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 5355-5364, 2023.
[41] Fathima, N., Petersen, J., Sautière, G., Wiggers, A., Pourreza, R. A Neural Video Codec with Spatial Rate-Distortion Control. In Proceedings of the Winter Conference on Applications of Computer Vision, pp. 5365-5374, 2023.
[42] Qi, L., Li, J., Li, B., Li, H., Lu, Y. Motion Information Propagation for Neural Video Compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6111-6120, 2023.
[43] Yang, R., Yang, Y., Marino, J., Mandt, S. Insights from generative modeling for neural video compression. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, issue 8, pp. 9908 – 9921, 2023.
[44] Belyaev, E. An Efficient Compressive Sensed Video Codec with Inter-Frame Decoding and Low-Complexity Intra-Frame Encoding. Sensors, vol. 23, no. 3, 1368, pp. 1-23, 2023.
[45] Kalsotra, R., Arora, S. Background subtraction for moving object detection: explorations of recent developments and challenges. The Visual Computer, vol. 38, no. 12, pp. 4151-4178, 2022.
[46] Mirzaei, B., Nezamabadi-pour, H., Abbasi-moghadam, D. An effective codebook initialization technique for LBG algorithm using subtractive clustering. In Proceedings of the Iranian Conference on Intelligent Systems, Feb 4, pp. 1-5, 2014.
[47] Ferreira, F., Leitao, H., Lopes, W., Madeiro, F. Hybrid firefly-Linde-Buzo-Gray algorithm for channel-optimized vector quantization codebook design. Integrated Computer-Aided Engineering, vol. 24, no. 3, pp. 297-314, 2017.
[48] Bao, W., Zhu, C. A secure and robust image encryption algorithm based on compressive sensing and DNA coding. Multimedia Tools and Applications, vol. 81, Issue 11, pp. 15977-15996, 2022.
[49] Zou, Y., Luo, C., Zhang, J. DIFLD: domain invariant feature learning to detect low-quality compressed face forgery images. Complex & Intelligent Systems. vol.10, Issue 1, pp.1-12, 2023.
[50] Song, Y., Yu, Z., Liu, E., Huang, H., Sun, K., Yin, F., Xu, K. Digital radio-over-fiber system based on differential pulse code modulation and space division multiplexing. Optics Letters, vol. 48, no. 7, pp. 1806-1809, 2023.
[51]Liu, R., Wang, C., Tang, A., Zhang, Y., Yu, Q. A twin delayed deep deterministic policy gradient-based energy management strategy for a battery-ultracapacitor electric vehicle considering driving condition recognition with learning vector quantization neural network. Journal of Energy Storage, vol. 71, 108147, pp.1-11, 2023.
[52] Zhang, K., Chen, Z., Yang, L., Liang, Y. Principal Component analysis (PCA) based sparrow search algorithm (SSA) for optimal learning vector quantized (LVQ) neural network for mechanical fault diagnosis of high voltage circuit breakers. Energy Reports, vol. 9, pp. 954-962, 2023.
[53] Radhika, K., Geetha, D. Retraction Note: Augmented Recurrence Hopping Based Run-Length Coding for Test Data Compression Applications. Wireless Personal Communications, vol. 128, 747, pp.1-20, 2023.
[54]Khairi, N., Jambek, A. Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application. International Journal of Nanoelectronics and Materials, vol. 14, pp. 191-197, 2021.
[55] Joy, H., Kounte, M., Chandrasekhar, A, Paul, M. Deep Learning Based Video Compression Techniques with Future Research Issues. Wireless Personal Communications. Vol. 131, pp. 2599–2625, 2023.
[56] Menon, V. Multi-resolution encoding and optimization for next generation video compression. arXiv preprint arXiv: 2301.12191 , pp.1-8, 2023.
[57][ Ghassab, V., Gonsalves, R., Mathur, S., Bouguila, N. Optimizing Video Compression with CNN-Based Autoencoders with Chroma Subsampling. SMPTE Motion Imaging Journal, vol. 132, no. 3, pp. 18-26, 2023.
[58] Alsmirat, M., Sharrab, Y., Tarawneh, M., Al-shboul, S., Sarhan, N. Video coding deep learning-based modeling for long life video streaming over next network generation. Cluster Computing, vol. 26, no. 2, pp. 1159-1167, 2023.
[59] Belodedov, M., Fonkants, R., Safin, R. Development of an Algorithm for Optimal Encoding of WAV Files Using Genetic Algorithms. In Proceedings of the International Youth Conference on Radio Electronics, Electrical and Power Engineering, Vol. 5, pp. 1-6, 2023.
[60] Choudhury, H., Sinha, N., Saikia, M. Nature inspired algorithms (NIA) for efficient video compression–A brief study. Engineering Science and Technology, an International Journal, vol. 23, no. 3, pp. 507-526, 2020.
[61] Nithin, S., Suresh, L., Krishnaveni, S., Muthukumar, P. Developing novel video coding model using modified dual-tree wavelet-based multi-resolution technique. Multimedia Systems, vol. 28, no. 2, pp. 643-657, 2022.
[62] Pandit, S., Shukla, P., Tiwari, A., Shukla, P., Maheshwari, M., Dubey, R. Review of video compression techniques based on fractal transform function and swarm intelligence. International Journal of Modern Physics, vol. 34, no. 08, 2050061, pp.1-15, 2020.
[63] Ravuri, R. Diamond Search Optimization-Based Technique for Motion Estimation in Video Compression. International Journal of e-Collaboration, vol. 19, no. 3, pp. 1-14, 2023.
[64]Veerasamy, B., Bharathi, B., Ahilan, A. Video compression using improved diamond search hybrid teaching and learning-based optimization model. The Imaging Science Journal, vol.71, Issue 6, pp.1-15, 2023.
