ICTACT Journal on Image and Video Processing

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Deep Reinforcement Learning-based Optimization and Enhancement of Multimedia Data : An Innovative Approach


Affiliations
1 Department of Mechanical Engineering, Indian Institute of Information Technology Kalyani, India
2 Department of Computer Science and Engineering, Joginpally B. R. Engineering College, India
3 Department of Electronics and Communication Engineering, CVR College of Engineering, India
4 Business Analytics Department, University of Rochester - Simon Business School, United States
     

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The rapid growth of multimedia data in various domains has necessitated the development of efficient techniques to enhance and optimize its quality. Traditional approaches often struggle to address the complexity and diversity of multimedia data, leading to suboptimal results. This paper presents a novel approach to tackle this challenge by leveraging the power of deep reinforcement learning (DRL). The proposed method utilizes DRL to learn and optimize multimedia data in an improvised manner. By employing a combination of convolutional neural networks and deep Q-networks, the model can effectively extract high-level features and make informed decisions to enhance the quality of multimedia data. The reinforcement learning framework enables the system to learn from its actions, continuously improving its performance through an iterative process. To evaluate the effectiveness of the proposed method, extensive experiments were conducted using a diverse set of multimedia datasets. The results demonstrate significant improvements in various quality metrics, including image resolution, video frame rate, and audio clarity. Additionally, the proposed approach exhibits robustness across different types of multimedia data, ensuring consistent enhancement performance across various domains. Furthermore, the computational efficiency of the proposed method is also highlighted, as it demonstrates faster convergence and lower computational overhead compared to traditional optimization methods. This makes the approach practical for real-time applications where multimedia data needs to be processed efficiently. Overall, this paper introduces an innovative framework that combines deep reinforcement learning with multimedia data optimization. The results indicate its potential for enhancing multimedia data quality, offering a promising solution to the challenges associated with traditional approaches. The proposed method not only improves the visual and auditory aspects of multimedia content but also provides a scalable and efficient solution for real-world applications in domains such as image processing, video streaming, and audio analysis.

Keywords

Deep Reinforcement Learning, Multimedia Data, Optimization, Enhancement, Convolutional Neural Networks, Deep Q-Networks, Quality Metrics, Computational Efficiency.
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  • V. Saravanan and C. Chandrasekar, “Qos-Continuous Live Media Streaming in Mobile Environment using VBR and Edge Network”, International Journal of Computer Applications, Vol. 53, No. 6, pp. 1-12, 2012.

  • K. Srihari and M. Masud, “Nature-Inspired-based Approach for Automated Cyberbullying Classification on Multimedia Social Networking”, Mathematical Problems in Engineering, Vol. 2021, pp. 1-12, 2021.

  • V. Saravanan and M. Rizvana, “Dual Mode Mpeg Steganography Scheme for Mobile and Fixed Devices”, International Journal of Engineering Research and Development, Vol. 6, pp. 23-27, 2013.

  • M.K. Gupta and P. Chandra, “Effects of Similarity/Distance Metrics on K-Means Algorithm with Respect to its Applications in IoT and Multimedia: A Review”, Multimedia Tools and Applications, Vol. 81, No. 26, pp. 37007-37032, 2022

  • S. Huang and M. Sun, “Deep Reinforcement Learning for Multimedia Analysis: A Survey”, ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 16, No. 3, pp. 1-29, 2020.

  • K. Zhang, “Reinforcement Learning for Image Restoration: A Comprehensive Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, No. 6, pp. 1760-1778, 2020.

  • J. Chen, “Deep Reinforcement Learning-Based Video Streaming: A Survey”, ACM Computing Surveys, Vol. 52, No. 6, pp. 1-35, 2019.

  • D. Xu, “Reinforcement Learning for Visual Object Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 3, pp. 590-604, 2018.

  • Z. Chen, “Reinforcement Learning for Audio-Based Applications: A Comprehensive Survey”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 5, No. 4, pp. 269-285, 2021.

  • Y. Wang, “Reinforcement Learning for Quality-Aware Multimedia Systems: A Survey”, ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 15, No. 3, pp. 1-29, 2019.

  • Y. Liu and Y. Zhang, “Deep Reinforcement Learning for Multimedia Quality Assessment: A Survey”, IEEE Transactions on Multimedia, Vol. 21, No. 12, pp. 3151-3165, 2019.

  • M. Kim and K. Chang, “Reinforcement Learning Based Adaptive Streaming Scheme with Edge Computing Assistance”, Sensors, Vol. 22, pp. 2171-2187, 2022.


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  • Deep Reinforcement Learning-based Optimization and Enhancement of Multimedia Data : An Innovative Approach

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Authors

Muruganantham Ponnusamy
Department of Mechanical Engineering, Indian Institute of Information Technology Kalyani, India
T. Prabakaran
Department of Computer Science and Engineering, Joginpally B. R. Engineering College, India
Swapna Thouti
Department of Electronics and Communication Engineering, CVR College of Engineering, India
Saivarshini Ravichandran
Business Analytics Department, University of Rochester - Simon Business School, United States

Abstract


The rapid growth of multimedia data in various domains has necessitated the development of efficient techniques to enhance and optimize its quality. Traditional approaches often struggle to address the complexity and diversity of multimedia data, leading to suboptimal results. This paper presents a novel approach to tackle this challenge by leveraging the power of deep reinforcement learning (DRL). The proposed method utilizes DRL to learn and optimize multimedia data in an improvised manner. By employing a combination of convolutional neural networks and deep Q-networks, the model can effectively extract high-level features and make informed decisions to enhance the quality of multimedia data. The reinforcement learning framework enables the system to learn from its actions, continuously improving its performance through an iterative process. To evaluate the effectiveness of the proposed method, extensive experiments were conducted using a diverse set of multimedia datasets. The results demonstrate significant improvements in various quality metrics, including image resolution, video frame rate, and audio clarity. Additionally, the proposed approach exhibits robustness across different types of multimedia data, ensuring consistent enhancement performance across various domains. Furthermore, the computational efficiency of the proposed method is also highlighted, as it demonstrates faster convergence and lower computational overhead compared to traditional optimization methods. This makes the approach practical for real-time applications where multimedia data needs to be processed efficiently. Overall, this paper introduces an innovative framework that combines deep reinforcement learning with multimedia data optimization. The results indicate its potential for enhancing multimedia data quality, offering a promising solution to the challenges associated with traditional approaches. The proposed method not only improves the visual and auditory aspects of multimedia content but also provides a scalable and efficient solution for real-world applications in domains such as image processing, video streaming, and audio analysis.

Keywords


Deep Reinforcement Learning, Multimedia Data, Optimization, Enhancement, Convolutional Neural Networks, Deep Q-Networks, Quality Metrics, Computational Efficiency.

References