Autoencoded Image Compression for Secure and Fast Transmission

TL;DR

This paper introduces an autoencoder-based image compression method that enhances transmission efficiency and security by combining dimensionality reduction with inherent encryption, achieving high-quality reconstruction and significant latency reduction.

Contribution

The paper presents a novel autoencoder architecture with a composite loss function that improves image compression, security, and transmission speed compared to traditional methods.

Findings

Achieves 97.5% SSIM in image reconstruction

Reduces average latency by 87.5%

Provides secure, efficient image transmission

Abstract

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer.