The goal of downscaling an image is to reduce its resolution to a lower resolution, while maintaining the visual characteristics of the original image. Recent learning-based algorithms have shown great improvement over conventional methods in preserving high-frequency information. However, they continue to suffer from various artifacts due to the lack of true high-low resolution image pairs in their training datasets. In this paper, an unsupervised image downscaler that preserves the high frequency content of the original image based on an autoencoder is presented. Specifically, the image downscaler is obtained by extracting the decoder of the developed autoencoder. Furthermore, we propose a preprocessing step that enables image downscaler to any arbitrary scales. Experimental results on five benchmark datasets reveal the qualitative and quantitative superiority of the proposed method at various scales compared to other methods.