Image downscaling is one of the most classical problems in computer vision that aims to preserve the visual appearance of the original image when it is resized to a smaller scale. Upscaling a small image back to its original size is a difficult and ill-posed problem due to information loss that arises in the downscaling process. In this paper, we present a novel technique called task-aware image downscaling to support an upscaling task. We propose an auto-encoder-based framework that enables joint learning of the downscaling network and the upscaling network to maximize the restoration performance. Our framework is efficient, and it can be generalized to handle an arbitrary image resizing operation.

Experimental results show that our task-aware downscaled images greatly improve the performance of the existing state-of-the-art super-resolution methods. In addition, realistic images can be recovered by recursively applying our scaling model up to an extreme scaling factor of x128. We also validate our model’s generalization capability by applying it to the task of image colorization.