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Exploiting View Synthesis for Super-multiview Video Compression

Pavel Nikitin
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Marco Cagnazzo
Joël Jung
Attilio Fiandrotti
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Super-multiview video consists in a 2D arrangement of cameras acquiring the same scene and it is a well-suited format for immersive and free navigation video services. However, the large number of acquired viewpoints calls for extremely effective compression tools. View synthesis allows to reconstruct a viewpoint using nearby cameras texture and depth information. In this work we explore the potential of recent advances in view synthesis algorithms to enhance the compression performances of super-multiview video. Towards this end we consider five methods that replace one viewpoint with a synthesized view, possibly enhanced with some side information. Our experiments suggest that, if the geometry information (i.e. depth map) is reliable, these methods have the potential to improve rate-distortion performance with respect to traditional approaches, at least for some specific content and configuration. Moreover, our results shed some light about how to further improve compression performance by integrating new view-synthesis prediction tools within a 3D video encoder.
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Dates and versions

hal-02364916 , version 1 (15-11-2019)



Pavel Nikitin, Marco Cagnazzo, Joël Jung, Attilio Fiandrotti. Exploiting View Synthesis for Super-multiview Video Compression. International Conference on Distributed Smart Cameras, Sep 2019, Trento, Italy. ⟨10.1145/3349801.3349820⟩. ⟨hal-02364916⟩
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