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

This paper addresses the video rescaling task, which arises from the needs ofadapting the video spatial resolution to suit individual viewing devices. Weaim to jointly optimize video downscaling and upscaling as a combined task.Most recent studies focus on image-based solutions, which do not considertemporal information. We present two joint optimization approaches based oninvertible neural networks with coupling layers. Our Long Short-Term MemoryVideo Rescaling Network (LSTM-VRN) leverages temporal information in thelow-resolution video to form an explicit prediction of the missinghigh-frequency information for upscaling. Our Multi-input Multi-output VideoRescaling Network (MIMO-VRN) proposes a new strategy for downscaling andupscaling a group of video frames simultaneously. Not only do they outperformthe image-based invertible model in terms of quantitative and qualitativeresults, but also show much improved upscaling quality than the video rescalingmethods without joint optimization. To our best knowledge, this work is thefirst attempt at the joint optimization of video downscaling and upscaling.

Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling