Task-Aware Image Downscaling

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, ill-posed problem due to information loss that arises in the downscaling process. In this paper, we present a novel operation 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 image, greatly improved the super-resolution performance of the previous state-of-the-art. In addition, realistic images can be recovered by recursively applying our scaling model up to an extreme scaling factor of x128. We validate our model's generalization capability by applying it to the task of image colorization.