Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
Deep Learning (DL) is revolutionizing video processing, as video is progressively key in daily life. Encoding and transmitting video effectively becomes challenging with fast content resolution and data volume. This research presents the most progressive method for Video Compressing (VC), using DL to enhance encoding and transmission efficiency, demonstrating the need for more cutting-edge methods in digital media. This work uses advanced Machine Learning (ML) to reduce video data size without compromising video quality, enhancing its suitability for high-definition streaming and videoconferencing. The algorithm uses Convolutional Neural Network (CNN)+Recurrent Neural Network (RNN) to improve video quality. CNN captures complex spatial details within each video frame, while LSTM relates across time. The proposed VC achieves high video quality rates compared to traditional methods like H.264 and H.265. It adapts in real-time and optimizes video bandwidth usage, making it useful for live streaming services and video conferencing. The VC has been tested extensively, demonstrating significant bit rate reduction while maintaining excellent video quality. It surpasses modern compression methods, making it a flexible solution to the increasing demands for the best video content. This invention in VC is expected to change digital media distribution for good.
Keywords
Video Compression, Deep Learning, Video Encoding, Video Transmission, Bandwidth Optimization.
K. Panneerselvam, K. Mahesh, V. L. Helen Josephine, and A. Ranjith Kumar, “Effective and Efficient Video Compression by the Deep Learning Techniques,” Computer Systems Science and Engineering, vol. 45, no. 2, pp. 1047–1061, 2023, doi: 10.32604/csse.2023.030513.
B. Liu, Y. Chen, S. Liu, and H.-S. Kim, “Deep Learning in Latent Space for Video Prediction and Compression,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 701–710, Jun. 2021, doi: 10.1109/cvpr46437.2021.00076.
S. Kunjiappan, L. K. Ramasamy, S. Kannan, P. Pavadai, P. Theivendren, and P. Palanisamy, “Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm,” Scientific Reports, vol. 14, no. 1, Jan. 2024, doi: 10.1038/s41598-023-49839-y.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Computational Intelligence and Neuroscience, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/7068349.
T. Gopalakrishnan, P. Sengottuvelan, A. Bharathi, R. Lokeshkumar, "An Approach to Webpage Prediction Method Using Variable Order Markov Model In Recommendation Systems," Journal of Internet Technology, vol. 19, no. 2, pp. 415-424, 2018.
Z. Zhou, K. Lin, Y. Cao, C.-N. Yang, and Y. Liu, “Near-Duplicate Image Detection System Using Coarse-to-Fine Matching Scheme Based on Global and Local CNN Features,” Mathematics, vol. 8, no. 4, p. 644, Apr. 2020, doi: 10.3390/math8040644.
N. Krishnadoss and L. Kumar Ramasamy, “A study on high dimensional big data using predictive data analytics model,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 1, p. 174, Apr. 2023, doi: 10.11591/ijeecs. v30.i1. pp174-182.
R. Lokeshkumar, O. Mishra, and S. Kalra, “Social media data analysis to predict mental state of users using machine learning techniques,” Journal of Education and Health Promotion, vol. 10, no. 1, p. 301, 2021, doi: 10.4103/jehp.jehp_446_20.
“An E-Commerce Based Personalized Health Product Recommendation System Using CNN-Bi-LSTM Model,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 6, pp. 398–410, Dec. 2023, doi: 10.22266/ijies2023.1231.33.
B. R. R. Reddy and R. L. Kumar, “A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU,” Computers, Materials & Continua, vol. 81, no. 3, pp. 4081–4107, 2024, doi: 10.32604/cmc.2024.057071.
U. Chadha et al., “Powder Bed Fusion via Machine Learning-Enabled Approaches,” Complexity, vol. 2023, pp. 1–25, Apr. 2023, doi: 10.1155/2023/9481790.
Mahalakshmi, R. L. Kumar, K. S. Ranjini, S. Sindhu, and R. Udhayakumar, “Efficient authenticated key establishment protocol for telecare medicine information systems,” Industrial, Mechanical and Electrical Engineering, vol. 2676, p. 020006, 2022, doi: 10.1063/5.0117522.
A. Atreya and D. O’Shea, “Novel lossy compression algorithms with stacked autoencoders,” Stanford University CS229, Tech. Rep., 2009.
P. Krishnamoorthy et al., “Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study,” IEEE Access, vol. 11, pp. 9389–9402, 2023, doi: 10.1109/access.2023.3236843.
R. K. Poluru and R. Lokeshkumar, “Meta-Heuristic MOALO Algorithm for Energy-Aware Clustering in the Internet of Things,” International Journal of Swarm Intelligence Research, vol. 12, no. 2, pp. 74–93, Apr. 2021, doi: 10.4018/ijsir.2021040105.
G. Toderici, S. M. O’Malley, S.-J. Hwang, D. Vincent, D. Minnen, S. Baluja, M. Covell, and R. Sukthankar, “Variable rate image compression with recurrent neural networks,” CoRR, vol. abs/1511.06085, 2015. [Online]. Available: http://arxiv.org/abs/1511.06085.
G. Toderici et al., “Full Resolution Image Compression with Recurrent Neural Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5435–5443, Jul. 2017, doi: 10.1109/cvpr.2017.577.
A. Srinivasan and G. Rohini, “RETRACTED ARTICLE: An Improvised video coding algorithm for deep learning-based video transmission using HEVC,” Soft Computing, vol. 23, no. 18, pp. 8503–8514, Jun. 2019, doi: 10.1007/s00500-019-04093-1.
S. Bouaafia, R. Khemiri, S. Messaoud, O. Ben Ahmed, and F. E. Sayadi, “Deep learning-based video quality enhancement for the new versatile video coding,” Neural Computing and Applications, vol. 34, no. 17, pp. 14135–14149, Sep. 2021, doi: 10.1007/s00521-021-06491-9.
F. Zaki, A. E. Mohamed, and S. G. Sayed, “CtuNet: A Deep Learning-based Framework for Fast CTU Partitioning of H265/HEVC Intra- coding,” Ain Shams Engineering Journal, vol. 12, no. 2, pp. 1859–1866, Jun. 2021, doi: 10.1016/j.asej.2021.01.001.
L. Zhao, H. Bai, A. Wang, and Y. Zhao, “Learning a virtual codec based on deep convolutional neural network to compress image,” Journal of Visual Communication and Image Representation, vol. 63, p. 102589, Aug. 2019, doi: 10.1016/j.jvcir.2019.102589.
A. Jacob, V. Pawar, V. Vishwakarma, and A. Mane, “Deep Learning Approach to Video Compression,” 2019 IEEE Bombay Section Signature Conference (IBSSC), pp. 1–5, Jul. 2019, doi: 10.1109/ibssc47189.2019.8973035.
N. Krishnadoss and L. K. Ramasamy, “Crop yield prediction with environmental and chemical variables using optimized ensemble predictive model in machine learning,” Environmental Research Communications, vol. 6, no. 10, p. 101001, Oct. 2024, doi: 10.1088/2515-7620/ad7e81.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Bairavel S, Lakshmi T K, Praveen Gugulothu, Abrar Ahmed Katiyan, Chandravadhana S and Helina Rajini Suresh;
Methodology: Praveen Gugulothu and Abrar Ahmed Katiyan;
Software: Lakshmi T K, Praveen Gugulothu and Abrar Ahmed Katiyan;
Data Curation: Chandravadhana S and Helina Rajini Suresh;
Writing- Original Draft Preparation: Bairavel S, Lakshmi T K, Praveen Gugulothu, Abrar Ahmed Katiyan, Chandravadhana S and Helina Rajini Suresh;
Visualization: Bairavel S, Lakshmi T K, Praveen Gugulothu and Abrar Ahmed Katiyan;
Investigation: Chandravadhana S and Helina Rajini Suresh;
Supervision: Praveen Gugulothu and Abrar Ahmed Katiyan;
Validation: Bairavel S, Lakshmi T K, Praveen Gugulothu and Abrar Ahmed Katiyan;
Writing- Reviewing and Editing: Bairavel S, Lakshmi T K, Praveen Gugulothu, Abrar Ahmed Katiyan, Chandravadhana S and Helina Rajini Suresh;
All authors reviewed the results and approved the final version of the manuscript.
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Bairavel S
Department of Artificial Intelligence and Data Science, KCG College of Technology, Chennai, Tamil Nadu, India.
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Cite this article
Bairavel S, Lakshmi T K, Praveen Gugulothu, Abrar Ahmed Katiyan, Chandravadhana S and Helina Rajini Suresh, “A Machine Learning-Based Video Compression for Effective Video Encoding and Transmission”, Journal of Machine and Computing, pp. 956-967, April 2025, doi: 10.53759/7669/jmc202505076.
