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Introduction

In recent years, the demand for efficient compression algorithms for image and video data has significantly increased due to the explosion of multimedia content on the internet and the limited storage capacity of devices. Traditional compression techniques such as JPEG and MPEG have been widely used, but they have limitations in terms of compression efficiency and image quality. Deep learning-based approaches have shown promising results in various computer vision tasks, including image and video compression. In this thesis, we aim to develop a deep learning-based system for image and video compression that can achieve higher compression ratios while maintaining high-quality reconstruction of the original data.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of image and video compression techniques
2.2 Deep learning in image and video compression
2.3 Convolutional neural networks for compression
2.4 Autoencoder-based compression techniques
2.5 Generative adversarial networks for compression
2.6 Perceptual-based compression methods
2.7 Reinforcement learning in compression
2.8 Comparison of traditional and deep learning-based compression
2.9 Challenges and future directions in deep learning compression
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Deep learning model architecture
3.3 Training process
3.4 Evaluation metrics
3.5 Benchmarking with existing compression methods
3.6 Parameter tuning and optimization
3.7 Experimental setup
3.8 Ethical considerations
3.9 Data analysis techniques
3.10 Summary of research methodology

Chapter 4: Discussion of Findings
4.1 Performance evaluation of the proposed system
4.2 Comparison with existing compression methods
4.3 Impact of different hyperparameters on compression efficiency
4.4 Analysis of image and video reconstruction quality
4.5 Computational complexity and resource efficiency
4.6 Real-world applications and scalability
4.7 User feedback and subjective evaluation
4.8 Limitations and challenges faced during the project
4.9 Future research directions
4.10 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field
5.3 Implications for image and video compression technologies
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview

Developing a deep learning-based system for image and video compression involves exploring the applications of deep learning algorithms in solving the challenge of efficient data compression while maintaining high-quality reconstruction. This thesis aims to investigate the feasibility and effectiveness of using deep learning models, such as convolutional neural networks and autoencoders, for image and video compression. The literature review will provide an overview of traditional compression techniques and discuss the recent advancements in deep learning-based compression methods.

The research methodology will outline the data collection process, model architecture design, training process, evaluation metrics, and experimental setup. The performance of the proposed system will be evaluated through benchmarking with existing compression methods and analyzing the reconstruction quality of compressed images and videos. The discussion of findings will present the results of the experiments, including the impact of different hyperparameters on compression efficiency, computational complexity, and user feedback.

The conclusion and summary chapter will summarize the key findings, contributions to the field, implications for image and video compression technologies, and recommendations for future research. Overall, this thesis aims to contribute to the development of more efficient and high-quality compression algorithms for multimedia data using deep learning techniques.

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