Abstract
While studies for objective and subjective evaluation of the visual quality of compressed 3D meshes has been discussed in the literature, those studies were covering the evaluation of 3D-meshes either created by 3D artists or generated by a computationally expensive reconstruction process applied on high quality 3D scans. With the advent of RGB-D sensors operating at high frame-rates and the utilization of fast 3D reconstruction algorithms, humans can be captured and reconstructed into a 3D representation in real-time, enabling new (tele-)immersive experiences. The produced 3D mesh content is structurally different in the two cases. The first type of content is nearly perfect and clean while the second type is much more irregular and noisy. Evaluating compression artifacts on this new type of immersive 3D media, constitutes a yet unexplored scientific area. In this paper, we conduct a survey to subjectively assess the perceived fidelity of 3D meshes subjected to compression using three open-source static 3D mesh codecs compared to the original uncompressed models. The subjective evaluation of the content is conducted in a Virtual Reality setting, using the forced-choice pairwise comparison methodology with existing reference. The results of this study are two-fold; first, the design of an experimental setup that can be used for the subjective evaluation of 3D media, and second, a mapping of the compared conditions to a continuous ranking scale. The latter can be used when selecting codecs and optimizing their compression parameters to achieve optimum balance between bandwidth and perceived quality in tele-immersive platforms.
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This work was supported and received funding from the EU H2020 Programme under Grant Agreement no 762111 VRTogether.
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Christaki, K. et al. (2019). Subjective Visual Quality Assessment of Immersive 3D Media Compressed by Open-Source Static 3D Mesh Codecs. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_7
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