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Introduction

In recent years, deep learning techniques have shown great potential in a wide range of applications including image recognition, natural language processing, and autonomous driving. One area where deep learning has made significant strides is in image compression. Image compression is essential for reducing the storage and transmission requirements of digital images, making them more efficient to use in various applications.

This thesis focuses on exploring the use of deep learning algorithms for image compression. Traditional image compression techniques such as JPEG and PNG have limitations in terms of quality and efficiency. Deep learning offers a promising alternative by leveraging neural networks to learn complex patterns in images and effectively compressing them while preserving image quality.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the study
1.3 Problem statement
1.4 Objectives of the study
1.5 Limitations of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of image compression techniques
2.2 Traditional image compression algorithms
2.3 Deep learning for image compression
2.4 Convolutional Neural Networks (CNNs) for image compression
2.5 Generative Adversarial Networks (GANs) for image compression
2.6 Autoencoders for image compression
2.7 Comparison of traditional and deep learning-based image compression techniques
2.8 Recent advances in deep learning for image compression
2.9 Challenges in deep learning-based image compression
2.10 Future directions in image compression using deep learning

Chapter 3: Research Methodology
3.1 Data collection
3.2 Preprocessing of images
3.3 Model selection
3.4 Training and evaluation
3.5 Performance metrics
3.6 Experimental design
3.7 Hyperparameter tuning
3.8 Validation and testing
3.9 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Performance comparison of deep learning-based image compression techniques
4.2 Analysis of compression efficiency
4.3 Evaluation of image quality
4.4 Impact of hyperparameters on compression performance
4.5 Computational complexity of deep learning models
4.6 Practical considerations for deploying deep learning-based image compression
4.7 Limitations of the proposed approach
4.8 Future research directions

Chapter 5: Conclusion
5.1 Summary of findings
5.2 Contributions of the study
5.3 Implications for practice
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview on Image Compression Using Deep Learning

Image compression is a crucial task in various applications such as image sharing, storage, and transmission. Traditional image compression algorithms like JPEG and PNG have been widely used but often suffer from limitations in compression efficiency and image quality. In recent years, deep learning techniques have shown remarkable progress in image compression, offering a promising alternative to traditional methods.

This thesis explores the use of deep learning algorithms for image compression, focusing on techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and autoencoders. The goal is to investigate the effectiveness of these deep learning models in compressing images while maintaining high-quality reconstruction.

The literature review provides an overview of traditional image compression techniques, deep learning approaches for image compression, and recent advances in the field. The research methodology outlines the data collection, preprocessing, model selection, training, and evaluation processes. The discussion of findings examines the performance, compression efficiency, image quality, and practical considerations of deep learning-based image compression.

Overall, this thesis aims to contribute to the understanding of image compression using deep learning and provide insights into future research directions in this exciting and rapidly evolving field.

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