CompressNet: Generative Compression at Extremely Low Bitrates
Shubham Dash, Giridharan Kumaravelu, Vijayakrishna Naganoor, Suraj Kiran Raman, Aditya Ramesh, Honglak Lee · 2020
Compressing images at extremely low bitrates (<; 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. With the increasing need to transfer large amounts of images with limited bandwidth, compressing images to very low sizes is a crucial task. However, the existing methods are not effective at extremely low bitrates. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bi-trates (<; 0.1 bpp). We benchmark the performance of our proposed method on the Cityscapes dataset, evaluating over different metrics at very low bitrates showing that our method outperforms the other state-of-the-art. In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods.