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Article Dans Une Revue ACM Transactions on Graphics Année : 2023

Textured Mesh Quality Assessment: Large-scale Dataset and Deep Learning-based Quality Metric

Yana Nehmé
Johanna Delanoy
Florent Dupont
Jean-Philippe Farrugia
Patrick Le Callet
Guillaume Lavoué

Résumé

Over the past decade, three-dimensional (3D) graphics have become highly detailed to mimic the real world, exploding their size and complexity. Certain applications and device constraints necessitate their simplification and/or lossy compression, which can degrade their visual quality. Thus, to ensure the best Quality of Experience, it is important to evaluate the visual quality to accurately drive the compression and find the right compromise between visual quality and data size. In this work, we focus on subjective and objective quality assessment of textured 3D meshes. We first establish a large-scale dataset, which includes 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity, and corrupted by combinations of five types of compression-based distortions applied on the geometry, texture mapping, and texture image of the meshes. This dataset contains over 343k distorted stimuli. We propose an approach to select a challenging subset of 3,000 stimuli for which we collected 148,929 quality judgments from over 4,500 participants in a large-scale crowdsourced subjective experiment. Leveraging our subject-rated dataset, a learning-based quality metric for 3D graphics was proposed. Our metric demonstrates state-of-the-art results on our dataset of textured meshes and on a dataset of distorted meshes with vertex colors. Finally, we present an application of our metric and dataset to explore the influence of distortion interactions and content characteristics on the perceived quality of compressed textured meshes.
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Dates et versions

hal-04120575 , version 1 (07-06-2023)

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Citer

Yana Nehmé, Johanna Delanoy, Florent Dupont, Jean-Philippe Farrugia, Patrick Le Callet, et al.. Textured Mesh Quality Assessment: Large-scale Dataset and Deep Learning-based Quality Metric. ACM Transactions on Graphics, 2023, 42 (3), pp.1-20. ⟨10.1145/3592786⟩. ⟨hal-04120575⟩
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