摘要
Immersive video streaming technologies improve Virtual Reality (VR) user experience by providing users more intuitive ways to move in simulated worlds, e.g., with 6 Degree-of-Freedom (6DoF) interaction mode. A naive method to achieve 6DoF is deploying cameras at numerous different positions and orientations that may be required based on users' movement, which unfortunately is expensive, tedious, and inefficient. A better solution for realizing 6DoF interactions is to synthesize target views on-The-fly from a limited number of source views. While such view synthesis is enabled by the recent Test Model for Immersive Video (TMIV) codec, TMIV dictates manually-composed configurations, which cannot exercise the tradeoff among video quality, decoding time, and bandwidth consumption. In this article, we study the limitation of TMIV and solve its configuration optimization problem by searching for the optimal configuration in a huge configuration space. We first identify the critical parameters in the TMIV configurations. Then, we introduce two Neural Network (NN)-based algorithms from two heterogeneous aspects: (i) a Convolutional Neural Network (CNN) algorithm solving a regression problem and (ii) a Deep Reinforcement Learning (DRL) algorithm solving a decision making problem, respectively. We conduct both objective and subjective experiments to evaluate the CNN and DRL algorithms on two diverse datasets: An equirectangular and a perspective projection dataset. The objective evaluations reveal that both algorithms significantly outperform the default configurations. In particular, with the equirectangular (perspective) projection dataset, the proposed algorithms only require 95% (23%) decoding time, stream 79% (23%) views, and improve the utility by 6% (73%) on average. The subjective evaluations confirm the proposed algorithms consume fewer resources while achieving comparable Quality of Experience (QoE) than the default and the optimal TMIV configurations.