Suitability of Single Image Super Resolution Models for Video Super Resolution

This project is an attempt to understand the suitability of the Single image super resolution models to video super resolution. Super Resolution refers to the process of enhancing the quality of low resolution images and video. Single image super resolution algorithms refer to those algorithms that can be applied on a single image to enhance its resolution. Whereas, video super resolution algorithms are those algorithms that are applied to a sequence of frames/images that constitute a video to enhance its resolution. In this paper we determine whether single image super resolution models can be applied to videos as well. When images are simply resized in Open CV, the traditional methods such as Interpolation are used which approximate the values of new pixels based on nearby pixel values which leave much to be desired in terms of visual quality, as the details (e.g. sharp edges) are often not preserved. We use deep learning techniques such as GANs (Generative Adversarial Networks) to train a model to output high resolution images from low resolution images. In this paper we analyse suitability of SRGAN and EDSR network architectures which are widely used and are popular for single image super resolution problem. We quantify the performance of these models, provide a method to evaluate and compare the models. We further draw a conclusion on the suitability and extent to which these models may be used for video super resolution. If found suitable this can have huge impact including but not limited to video compression, embedded models in end devices to enhance video output quality.