A No-Reference Machine Learning Based Video Quality Predictor
2013 (English)Conference paper (Refereed)
The growing need of quick and online estimation of video quality necessitates the study of new frontiers in the area of no-reference visual quality assessment. Bitstream-layer model based video quality predictors use certain visual quality relevant features from the encoded video bitstream to estimate the quality. Contemporary techniques vary in the number and nature of features employed and the use of prediction model. This paper proposes a prediction model with a concise set of bitstream based features and a machine learning based quality predictor. Several full reference quality metrics are predicted using the proposed model with reasonably good levels of accuracy, monotonicity and consistency.
Place, publisher, year, edition, pages
Klagenfurt am Wörthersee: IEEE , 2013.
Video Quality, H.264/AVC, Bitstream Features, No-Reference, Support Vector Machine
IdentifiersURN: urn:nbn:se:bth-6686DOI: 10.1109/QoMEX.2013.6603233ISI: 000331828000042Local ID: oai:bth.se:forskinfoF4C25327032FF159C1257BF3002DFA49OAI: oai:DiVA.org:bth-6686DiVA: diva2:834210
Fifth International Workshop on Quality of Multimedia Experience (QoMEX)