vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffb3c93200000042ae130001000600 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.
Muruganantham Ponnusamy1, T. Prabakaran2, Swapna Thouti3, Saivarshini Ravichandran4
Indian Institute of Information Technology Kalyani, India1, Joginpally B. R. Engineering College, India2, CVR College of Engineering, India3, University of Rochester - Simon Business School, United States4
Deep Reinforcement Learning, Multimedia Data, Optimization, Enhancement, Convolutional Neural Networks, Deep Q-Networks, Quality Metrics, Computational Efficiency
| January | February | March | April | May | June | July | August | September | October | November | December |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 4 , Pages: 3013 - 3020 )
Date of Publication :
May 2023
Page Views :
126
Full Text Views :
6
|