This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Deep learning methods are increasingly being applied in the optimisation of video
compression algorithms and can achieve significantly enhanced coding gains, compared to …

In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet,
for post-processing (PP) and in-loop filtering (ILF) in the context of video compression. This …

In recent years, video compression techniques have been significantly challenged by the
rapidly increased demands associated with high quality and immersive video content …

In recent years, deep learning techniques have shown significant potential for improving
video quality assessment (VQA), achieving higher correlation with subjective opinions …

This paper provides a survey of the latest developments in visual signal coding and
processing with generative models. Specifically, our focus is on presenting the advancement …

We present a new video compression framework (ViSTRA2) which exploits adaptation of
spatial resolution and effective bit depth, down-sampling these parameters at the encoder …

In recent years, end-to-end learnt video codecs have demonstrated their potential to
compete with conventional coding algorithms in term of compression efficiency. However …

We propose a new Generative Adversarial Network for Compressed Video frame quality
Enhancement (CVEGAN). The CVEGAN generator benefits from the use of a novel Mul 2 …

Resolution and effective bit depth (EBD) adaptation have been recently utilised in video
compression to improve coding efficiency. This type of approach dynamically reduces …