Guest Editorial Advances in Generative Visual Signal Coding and Processing

Abstract

This special issue of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to demonstrating the latest developments in algorithms, implementations, and applications related to visual signal coding and processing with generative models. In recent years, generative models have emerged as one of the most significant and rapidly developing areas of research in artificial intelligence. They have proved to be an important instrument for advancing research in AI-based visual signal coding and processing. For instance, the variational autoencoder (VAE) has been used as a fundamental framework for end-to-end learned image coding, the autoregressive (AR) model has been extensively studied for efficient entropy coding, and the generative adversarial network (GAN) has been utilized frequently to enhance the subjective quality of coding schemes. Meanwhile, generative models have also been explored in various visual signal processing tasks, including quality assessment, restoration, enhancement, editing, and interpolation.