LEARNING ADJUSTABLE IMAGE RESCALING WITH JOINT OPTIMIZATION OF PERCEPTION AND DISTORTION

The performance of image super-resolution (SR) have been greatly advanced by deep learning techniques recently. Most models are only optimized for the ill-posed upscaling task while assuming a predefined downscaling kernel for low-resolution (LR) inputs. Additionally, there exists a conflict between the objective and perceptual qualities of upscaled outputs for optimizing these models. To achieve an effective trade-off between these two qualities, the current methods are either inflexible as the model is optimized for a fixed trade-off, or inefficient as it needs to interpolate weights or images from two separately trained models. Based on the invertible rescaling net (IRN) which learns image downscaling and upscaling together, we propose a joint optimization method to train just one model that could achieve adjustable trade-off between perception and distortion for upscaling at inference time. Additionally, it?s shown in experiments that this jointly optimized model could produce results with better accuracy while maintaining high perceptual quality compared to one optimized for perceptual quality only.