Video Content Assessment Based on Perceptual Quality Indicators (A Popularity Predictor Model for YouTube Videos)
Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Various video quality assessment methods have been developed to assess the quality of videos worldwide. Most of these methods and metrics focus on certain impairments and degradation that are caused during processing or transmission. Only very few do focus on metrics of video quality based on their content. Those few are also limited in that they are specifically designed and developed to deal on a specific parameter. It is customary to hear about the most popular music and clips being announced on the mainstream media based on the hits they have in a specific period. Attempts have been made to develop algorithms that predict the hits of music singles. Studies show that a subject’s liking or disliking of the contents of videos influence subjective assessments. In this thesis, we have considered opinion deviations of viewers’ as gradient function. The amount of differences between assessments of a subject for a certain video defines the order of deviation which is called as gradient degree. The accumulated numbers of all subjects’ assessments for a certain video and for each gradient degree defines the amplitude of the related gradient degree. Video popularity sometimes is related to its perceptual quality, due to this reason; we used perceptual quality indicators as video content assessment categories. In this thesis, we have presented a new methodology that can be used to predict the subjective video content perception of viewers. In this paper, we have also proposed a model that uses the new gradient methodology to predict popularity of streaming videos. With the proposed model, we have found global weighting constants for predicting video popularity of YouTube video database. In this thesis we concluded that, we can predict the video quality of a video package from the decision consistency (inconsistency) of a certain number of people using the PQI categories. Having a predefined, but enough, number of people we can predict the acceptance of a video before we release to the wider population.
Place, publisher, year, edition, pages
2014. , 40 p.
Video Content Assessment, Perceptual Quality Indicators, YouTube Popularity Predictor, Semantic Quality Assessment
Media and Communications Telecommunications Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:bth-5739Local ID: oai:bth.se:arkivex2BCC4FFFCB921549C1257CF900322E3DOAI: oai:DiVA.org:bth-5739DiVA: diva2:833138
UppsokSocial and Behavioural Science, Law
Subbareddy Darukumalli: +467671613542015-04-222014-06-162015-06-30Bibliographically approved