Autoencoder-based image compression for wireless sensor networks

Abstract

This work presents an image compression technique designed for wireless sensor networks (WSNs), leveraging autoencoders and incorporating an error-bound mechanism. The algorithm strategically reduces redundancies in image data, conserving energy by exploiting spatial and temporal correlations within sampled image data through autoencoder features. Precise control over the distortion level in reconstructed images is achieved via the error-bound mechanism, establishing equilibrium between compression rate and reconstruction error. Evaluation results demonstrate comparable image reconstruction fidelity to existing methods (JPEG, JPEG2000, HDPhoto, and an existing Rate-Distortion Balanced approach). The proposed algorithm achieves superior image reconstruction quality at compression ratio rates exceeding 70%, emphasizing a fundamental approach prioritizing heightened reconstructed image quality while balancing compression ratio, distortion, and energy efficiency. Notably, a substantial 50% reduction in overall energy consumption is realized at a compression rate of 38.6%.