PG2021 Short Papers, Posters, and Work-in-Progress Papers

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https://diglib.eg.org/handle/10.2312/2633094

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Browsing PG2021 Short Papers, Posters, and Work-in-Progress Papers by Subject "Image compression"

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    CSLF: Cube Surface Light Field and Its Sampling, Compression, Real-Time Rendering
    (The Eurographics Association, 2021) Ai, Xiaofei; Wang, Yigang; Kou, Simin; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    Light field is gaining both research and commercial interests since it has the potential to produce view-dependent and photorealistic effects for virtual and augmented reality. In this paper, we further explore the light field and presents a novel parameterization that permits 1) effectively sampling the light field of an object with unknown geometry, 2) efficiently compressing and 3) real-time rendering from arbitrary viewpoints. A novel, key element in our parameterization is that we use the intersections of the light rays and a general cube surface to parameterize the four-dimensional light field, constructing the cube surface light field (CSLF). We resolve the huge data amount problem in CSLF by uniformly decimating the viewpoint space to form a set of key views which are then converted into a pseudo video sequence and compressed using the high efficiency video coding encoder. To render the CSLF, we employ a ray casting approach and draw a polygonal mesh, enabling real-time generating arbitrary views from the outside of the cube surface. We build the CSLF datasets and extensively evaluate our parameterization from the sampling, compression and rendering. Results show that the cube surface parameterization can simultaneously achieve the above three characteristics, indicating the potentiality in practical virtual and augmented reality.
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    Volumetric Video Streaming Data Reduction Method Using Front-mesh 3D Data
    (The Eurographics Association, 2021) Zhao, Xiaotian; Okuyama, Takafumi; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    Volumetric video contents are attracting much attention across various industries for their six-degrees-of-freedom (6DoF) viewing experience. However, in terms of streaming, volumetric video contents still present challenges such as high data volume and bandwidth consumption, which results in high stress on the network. To solve this issue, we propose a method using frontmesh 3D data to reduce the data size without affecting the visual quality much from a user's perspective. The proposed method also reduces decoding and import time on the client side, which enables faster playback of 3D data. We evaluated our method in terms of data reduction and computation complexity and conducted a qualitative analysis by comparing rendering results with reference data at different diagonal angles. Our method successfully reduces data volume and computation complexity with minimal visual quality loss.