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

Recent years have seen steady growth in the popularity and availability ofHigh Dynamic Range (HDR) content, particularly videos, streamed over theinternet. As a result, assessing the subjective quality of HDR videos, whichare generally subjected to compression, is of increasing importance. Inparticular, we target the task of full-reference quality assessment ofcompressed HDR videos. The state-of-the-art (SOTA) approach HDRMAX involvesaugmenting off-the-shelf video quality models, such as VMAF, with featurescomputed on non-linearly transformed video frames. However, HDRMAX increasesthe computational complexity of models like VMAF. Here, we show that anefficient class of video quality prediction models named FUNQUE+ achieves SOTAaccuracy. This shows that the FUNQUE+ models are flexible alternatives to VMAFthat achieve higher HDR video quality prediction accuracy at lowercomputational cost.

A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos