Optimizing Data Compression Algorithms Using Deep Learning Techniques

Thesis Abstract

The exponential growth of digital data in recent years has necessitated the development of more efficient data compression techniques to optimize storage and transmission resources, thereby addressing escalating bandwidth and storage costs faced by contemporary ICT infrastructures. Traditional compression algorithms, while effective to a degree, often encounter limitations in adapting to the diverse and complex nature of modern datasets, prompting an urgent need for innovative approaches capable of enhancing compression efficiency without compromising data fidelity. This study aims to develop and validate an optimized data compression framework leveraging deep learning techniques, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to improve compression rates while maintaining high-quality reconstruction. The primary objectives include (1) evaluating the limitations of existing classical data compression algorithms such as Huffman, Lempel-Ziv-Welch (LZW), and JPEG in handling large-scale and heterogeneous datasets; (2) designing novel deep learning-based compression models employing autoencoders and generative adversarial networks (GANs); (3) empirically comparing the performance of the proposed models against conventional algorithms based on compression ratio, processing time, and reconstructed data fidelity; and (4) establishing an optimized deep learning framework adaptable across various data modalities, including images, audio, and text. The research adopts a quantitative descriptive research design, employing experimental modeling to assess and compare the effectiveness of different compression algorithms. The target population encompasses publicly available datasets comprising 10,000 high-resolution images, 5,000 hours of audio recordings, and extensive textual corpora to ensure the generalizability of findings. A stratified random sampling technique is employed to select representative data subsets, ensuring coverages of different data types and complexities. Data collection involves gathering benchmark datasets obtained from repositories such as ImageNet, LibriSpeech, and Project Gutenberg, which serve as standard testbeds for compression performance evaluation. Data analysis utilizes a combination of advanced analytical techniques, including regression analysis and analysis of variance (ANOVA), to measure statistically significant differences in compression efficacy across models. Model performance metrics, such as compression ratio, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean opinion score (MOS) for subjective quality assessment, are analyzed through sophisticated software tools like MATLAB and TensorFlow. Model training incorporates hyperparameter tuning and cross-validation to prevent overfitting, while neural network architectures are optimized using grid search techniques. Ethical considerations involve adherence to data privacy standards and secure handling of datasets to prevent misuse or bias. Expected key findings suggest that deep learning-based algorithms, particularly autoencoder architectures combined with GAN frameworks, significantly outperform traditional algorithms in achieving higher compression ratios while preserving data integrity, especially for complex and large-scale datasets. The models are anticipated to demonstrate robust generalizability across different data types and applications, with faster processing times owing to parallel computing capabilities enabled by GPU acceleration. This study contributes novel insights into the application of advanced neural network architectures for data compression, filling a critical research gap where conventional methods plateau in performance. It establishes a comprehensive, scalable deep learning framework that can be integrated into existing ICT systems to enhance storage efficiency and transmission speed, especially in bandwidth-constrained environments. The findings provide empirical evidence for the feasibility and superiority of deep learning models in data compression tasks, offering a foundation for future research on adaptive, real-time compression solutions across diverse data modalities. The study concludes that integrating deep learning techniques into data compression processes yields significant improvements over traditional approaches, with implications for optimizing ICT infrastructure and many data-driven applications. Recommendations include further investigation into the deployment of these models in real-time systems, exploration of hybrid approaches combining classical and deep learning methods, and extending the research to other emerging data types such as 3D models and sensor data for comprehensive digital ecosystem optimization.