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

A video compression framework based on spatio-temporal resolution adaptation (ViSTRA) is
proposed, which dynamically resamples the input video spatially and temporally during …

The increase of video content and video resolution drive more exploration of video
compression techniques recently. Meanwhile, learning-based video compression is …

Recently, convolutional neural network (CNN)-based methods have achieved remarkable
progress in image and video super-resolution, which inspires research on down-/up …

With the increase of resolution and frame rate, videos can provide users with a better
viewing experience. However, the amount of data is also greatly increased, making it much …

A challenge that many video providers face is the heterogeneity of networks and display
devices for streaming, as well as dealing with a wide variety of content with different …

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 this paper we present the BVI-SR video database, which contains 24 unique video
sequences at a range of spatial resolutions up to UHD-1 (3840p). These sequences were …

Deep learning techniques have been applied in the context of image super-resolution (SR),
achieving remarkable advances in terms of reconstruction performance. Existing techniques …

Video compression plays an essential role in many multimedia applications, and
compression efficiency is highly related to video content. In this paper, based on video …