A machine learning framework for full-reference 3D shape quality assessment

To decide whether the perceived quality of a mesh is influenced by a certain modification such as compression or simplification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they prese...

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Bibliographic Details
Published in:The Visual computer 2020-01, Vol.36 (1), p.127-139
Main Authors: Yildiz, Zeynep Cipiloglu, Oztireli, A. Cengiz, Capin, Tolga
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Graphics
Computer Science
Crowdsourcing
Datasets
Deep learning
Design
Finite element method
Image Processing and Computer Vision
Machine learning
Methods
Original Article
Parameters
Perception
Quality assessment
Semantics
Visual perception
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Summary:To decide whether the perceived quality of a mesh is influenced by a certain modification such as compression or simplification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they present efficient solutions to many complex problems. However, these techniques are not much utilized in the field of 3D shape perception. We propose a novel machine learning-based approach for evaluating the visual quality of 3D static meshes. The novelty of our study lies in incorporating crowdsourcing in a machine learning framework for visual quality evaluation. We deliberate that this is an elegant way since modeling human visual system processes is a tedious task and requires tuning many parameters. We employ crowdsourcing methodology for collecting data of quality evaluations and metric learning for drawing the best parameters that well correlate with the human perception. Experimental validation of the proposed metric reveals a promising correlation between the metric output and human perception. Results of our crowdsourcing experiments are publicly available for the community.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-018-1592-9