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

We have developed reduced reference parametric models for estimatingperceived quality in audiovisual multimedia services. We have created 144unique configurations for audiovisual content including various application andnetwork parameters such as bitrates and distortions in terms of bandwidth,packet loss rate and jitter. To generate the data needed for model training andvalidation we have tasked 24 subjects, in a controlled environment, to rate theoverall audiovisual quality on the absolute category rating (ACR) 5-levelquality scale. We have developed models using Random Forest and Neural Networkbased machine learning methods in order to estimate Mean Opinion Scores (MOS)values. We have used information retrieved from the packet headers and sideinformation provided as network parameters for model training. Random Forestbased models have performed better in terms of Root Mean Square Error (RMSE)and Pearson correlation coefficient. The side information proved to be veryeffective in developing the model. We have found that, while the modelperformance might be improved by replacing the side information with moreaccurate bit stream level measurements, they are performing well in estimatingperceived quality in audiovisual multimedia services.

Towards Reduced Reference Parametric Models for Estimating Audiovisual Quality in Multimedia Services