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Highly parallel steered mixture-of-experts rendering at pixel-level for image and light field data

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Abstract

A novel image approximation framework called steered mixture-of-experts (SMoE) was recently presented. SMoE has multiple applications in coding, scale-conversion, and general processing of image modalities. In particular, it has strong potential for coding and streaming higher dimensional image modalities that are necessary to leverage full translational and rotational freedom (6 degrees-of-freedom) in virtual reality for camera captured images. In this paper, we analyze the rendering performance of SMoE for 2D images and 4D light fields. Two different GPU implementations that parallelize the SMoE regression step at pixel-level are presented, including experimental evaluations based on rendering performance and quality. In this paper it is shown that on appropriate hardware, an OpenCL implementation can achieve 85 fps and 22 fps for, respectively, 1080p and 4K renderings of large models with more than 100,000 of Gaussian kernels.

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Funding

The research activities described in this paper were funded by IDLab (Ghent University-imec), Flanders Innovation and Entrepreneurship (VLAIO), the Fund for Scientific Research Flanders (FWO Flanders), and the European Union.

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Authors and Affiliations

  1. Department of Electronic and Information Systems, Ghent University-imec-IDLab, Technologiepark-Zwijnaarde 19, 9052, Ghent, Belgium

    Vasileios Avramelos, Ruben Verhack, Ignace Saenen, Glenn Van Wallendael & Peter Lambert

  2. Department of Telecommunications and Information Processing, Ghent University-imec-IPI, Sint-Pietersnieuwstraat 41B, 9000, Ghent, Belgium

    Bart Goossens

  3. Communications Systems Group, Technische Universität Berlin, Einsteinufer 17, 10587, Berlin, Germany

    Ruben Verhack

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  1. Vasileios Avramelos
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  2. Ruben Verhack
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  4. Glenn Van Wallendael
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  6. Peter Lambert
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Correspondence to Vasileios Avramelos.

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Avramelos, V., Verhack, R., Saenen, I. et al. Highly parallel steered mixture-of-experts rendering at pixel-level for image and light field data. J Real-Time Image Proc 17, 931–947 (2020). https://doi.org/10.1007/s11554-018-0843-3

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