Methods of video quality-improving

Abstract

Video content has become integral to our daily lives, but poor video quality can significantly reduce viewers' experience and engagement. Various super-resolution methods are used to correct this, thereby reconstructing high-resolution videos from low-resolution ones. Two main categories of super-resolution methods exist traditional image processing and deep learning-based techniques. Deep learning-based techniques, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs), have shown great promise in enhancing video quality. The article discusses multiple adaptations of contemporary deep learning models to enhance video resolution. It also briefly explains the framework's design and implementation aspects. Lastly, the paper presents an overview and comparative analysis of the VSR techniques' efficiency on various benchmark datasets. At the same time, the paper describes potential challenges when choosing training sets; performance metrics, which can be used to compare different algorithms quantitatively. This work does not describe absolutely all existing VSR methods, but it is expected to contribute to the development of recent research in this field and potentially deepen our understanding of deep learning-based VSR methods, as well as stimulate further research in this area. In this work, new solutions for improving the performance of the methods are proposed, in particular, new quality metrics and datasets for model training. Overall, AI-based methods for VSR are becoming increasingly crucial with the rising demand for high-quality video content