000254987 001__ 254987
000254987 005__ 20190619220025.0
000254987 020__ $$a978-1-5386-1737-3
000254987 0247_ $$a10.1109/ICME.2018.8486512$$2doi
000254987 02470 $$2ScopusID$$a+
000254987 037__ $$aCONF
000254987 245__ $$aPoint Cloud Quality Assessment Metric Based on Angular Similarity
000254987 260__ $$c2018
000254987 269__ $$a2018
000254987 300__ $$a6
000254987 336__ $$aConference Papers
000254987 520__ $$aThe rise of immersive technologies has been recently fuelled by emerging applications which employ advanced content representations. Among various alternatives, point clouds denote a promising solution which has recently drawn a significant amount of interest, as witnessed by the latest activities of standardization committees. However, subjective and objective quality assessments for this type of content still remain an open problem. In this paper, we introduce a simple yet efficient objective metric to capture perceptual degradations of a distorted point cloud. Correlation with subjective quality assessment scores carried out by human subjects shows the proposed metric to be superior to the state of the art in terms of predicting the visual quality of point clouds under realistic types of distortions, such as octree-based compression.
000254987 6531_ $$apoint cloud
000254987 6531_ $$aobjective quality metrics
000254987 6531_ $$aquality assessment
000254987 700__ $$g274471$$aAlexiou, Evangelos
$$0250078
000254987 700__ $$0240223$$aEbrahimi, Touradj
$$g105043
000254987 7112_ $$dJuly 23-27, 2018$$cSan Diego, California, USA$$a2018 IEEE International Conference on Multimedia and Expo (ICME)
000254987 773__ $$t2018 IEEE International Conference on Multimedia and Expo (ICME)
000254987 8560_ $$fevangelos.alexiou@epfl.ch
000254987 8564_ $$uhttps://infoscience.epfl.ch/record/254987/files/2018-ICME.pdf$$s514225
000254987 8564_ $$uhttps://infoscience.epfl.ch/record/254987/files/2018-ICME.pdf?subformat=pdfa$$s2350463$$xpdfa
000254987 909C0 $$xUS02700$$pMMSPL$$mtouradj.ebrahimi@epfl.ch$$0252077$$zMarselli, Béatrice$$xU11889
000254987 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.epfl.ch:254987
000254987 960__ $$aevangelos.alexiou@epfl.ch
000254987 961__ $$alaurence.gauvin@epfl.ch
000254987 973__ $$aEPFL$$rREVIEWED
000254987 980__ $$aCONF
000254987 981__ $$aoverwrite