An Efficient Image Compression Technique using Long Short-Term Memory Networks (LSTM)

Abstract

The emergence of big data has imposed significant challenges on data storage and transmission. One pressing issue is leveraging deep learning techniques to achieve superior compression ratios and enhance image quality. Recurrent Neural Networks (RNNs) offer a promising avenue for controlling image bit rates iteratively, thereby enhancing compression performance. However, integrating Long Short-Term Memory (LSTM) into RNNs to address long-term dependencies increases model complexity. To expedite training and enhance image reconstruction quality, this study proposes several innovations.Initially, we enhance the activation function within LSTM to more effectively manage information retention and omission, thereby reducing parameter count and expediting training. Additionally, we introduce an image recovery block within the decoder to reconstruct high-resolution images. Finally, to expedite loss convergence, we replace L1 loss with SmoothL1 loss. Experimental outcomes demonstrate the efficacy of our approach, showcasing higher compression ratios