End-to-End Deep Learning Models for Real-Time Image Compression

Abstract

The exponential growth in data generation has intensified the need for efficient image compression methods, especially in real-time applications such as video streaming, medical imaging, and video conferencing. Traditional image compression techniques, such as JPEG and PNG, have limitations in terms of compression efficiency and image quality, particularly at high compression ratios. In this paper, we propose an end-to-end deep learning model for real-time image compression, utilizing a Convolutional Neural Network (CNN) architecture. The model includes an encoder-decoder structure that teaches to compress and reconstruct images with minimal loss of perceptual quality. The model is trained in an end-to-end fashion, optimizing the compression process while maintaining high visual fidelity. We evaluate the performance of the model using several metrics, including Compression Ratio, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), and compare it to traditional compression methods like JPEG and PNG. The results demonstrate that our model outperforms both JPEG and PNG in terms of compression efficiency and image quality, achieving a compression ratio of 7:1, an average PSNR of 40 dB, and an SSIM of 0.95. These results indicate that the proposed model can effectively compress images without introducing significant perceptual degradation. Despite the promising results, the deep learning model is computationally intensive, especially during training and inference. To address this, further optimizations such as model pruning and hardware acceleration can be explored to enhance real-time performance. Overall, this research shows the potential of deep learning-based image compression as a viable solution for real-time applications that require both high compression ratios and minimal quality loss