Performance analysis of multiview video compression based on MIV and VVC multilayer

Abstract

To represent immersive media providing six degree-of-freedom experience, moving picture experts group (MPEG) immersive video (MIV) was developed to compress multiview videos. Meanwhile, the state-of-the-art versatile video coding (VVC) also supports multilayer (ML) functionality, enabling the coding of multiview videos. In this study, we designed experimental conditions to assess the performance of these two state-of-the-art standards in terms of objective and subjective quality. We observe that their performances are highly dependent on the conditions of the input source, such as the camera arrangement and the ratio of input views to all views. VVC-ML is efficient when the input source is captured by a planar camera arrangement and many input views are used. Conversely, MIV outperforms VVC-ML when the camera arrangement is non-planar and the ratio of input views to all views is low. In terms of the subjective quality of the synthesized view, VVC-ML causes severe rendering artifacts such as holes when occluded regions exist among the input views, whereas MIV reconstructs the occluded regions correctly but induces rendering artifacts with rectangular shapes at low bitrates.