English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Video compression is a central feature of the modern internet poweringtechnologies from social media to video conferencing. While video compressioncontinues to mature, for many compression settings, quality loss is stillnoticeable. These settings nevertheless have important applications to theefficient transmission of videos over bandwidth constrained or otherwiseunstable connections. In this work, we develop a deep learning architecturecapable of restoring detail to compressed videos which leverages the underlyingstructure and motion information embedded in the video bitstream. We show thatthis improves restoration accuracy compared to prior compression correctionmethods and is competitive when compared with recent deep-learning-based videocompression methods on rate-distortion while achieving higher throughput.Furthermore, we condition our model on quantization data which is readilyavailable in the bitstream. This allows our single model to handle a variety ofdifferent compression quality settings which required an ensemble of models inprior work.

Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement