Lightweight Autoencoder with Hierarchical Priors for Learned Image Compression

Image compression has become an important task for reducing storage and transmission costs. However, recent models for learned image compression have been developed to increase the network’s number of layers and channels to achieve better visual effects. This resulted in higher computing and memory resources, making deploying the model on compute-constrained platforms such as wireless devices impractical. In this paper, we propose a lightweight autoencoder with hierarchical priors. The lightweight autoencoder reduces the model’s parameter size and calculation amount based on ensuring high fidelity and low bit rates of the image. Simulation results indicate the proposed model yields a smaller size: the parameters are reduced by 81.66%, and the calculation amount is reduced by 94.7% over the benchmark. Besides, the proposed model results in a speed improvement of 200 times. At the same time, our model achieves nearly the same performance as the baseline on MS-SSIM and LPIPS distortion metrics.