6533b827fe1ef96bd128669c
RESEARCH PRODUCT
A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network
Ilyass AbouelazizHocine CherifiMohammed El Hassounisubject
feature learning[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencemedia_common.quotation_subjectFeature extractiondistorted meshGRNNmean curvature02 engineering and technologyMachine learningcomputer.software_genreCurvaturevisual aspect representation[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDistortioncomputational method0202 electrical engineering electronic engineering information engineeringFeature (machine learning)computational geometrymean opinion scoresQuality (business)Polygon meshmedia_commonArtificial neural networkbusiness.industrycompetitive scores Author Keywords Blind mesh visual quality assessmentperceptual feature020207 software engineeringregression analysis INSPEC: Non-Controlled Indexing curvature based methodblind mesh visual quality assessmentno-reference quality assessmentvisual qualityVisualizationgeneral regression neural network traininggeneral regression neural networkmesh generationneural netssubject scoreshuman perceived quality predictionhuman subjective scores020201 artificial intelligence & image processinglearning (artificial intelligence)Artificial intelligencepredicted objective scoresbusiness3D meshcomputerdescription
International audience; No-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully assesses the visual quality, in addition, the experimental results show that the proposed method provides good correlations with the subject scores and competitive scores comparing to some influential and effective full and reduced reference existing metrics.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-11-28 |