Recurrent Neural Network-Based Lossless Data Compression

In the digital era, the exponential growth of image data across various sectors, including medical imaging, satellite imagery, and digital archives, necessitates advanced solutions for efficient storage and transmission. JPEG (Joint Photographic Experts Group), one of the most ubiquitous image compression standards, plays a pivotal role in managing digital image data. However, its lossy nature, which compromises image integrity for the sake of compression, is unsuitable for applications requiring precise image reproduction. This project explores the potential of Recurrent Neural Networks (RNNs) to achieve lossless compression of JPEG images, aiming to retain image quality while reducing file size significantly. This study explores the application of RNNs for efficient image compression, aiming to leverage the temporal dependency modelling capabilities of RNNs to maintain high image quality while achieving significant reductions in file size. Traditional image compression techniques often struggle to balance compression ratio and image fidelity, especially in contexts requiring real-time processing and transmission. RNNs, known for their proficiency in handling sequential data, offer a novel approach to image compression by treating image pixels as a sequence achieved an accuracy of 95.83% when compared to CNN. This perspective allows for more sophisticated pattern recognition and prediction, potentially surpassing the limitations of conventional methods.