Viewport-Dependent Streaming (VDS) is a preferred way in practice to deliver the omnidirectional videos, of which a High-Quality (HQ) scale is applied for the content in current viewport but a Low-Quality (LQ) scale elsewhere. Quality adaptation or refinement happens after users stabilize their fixations to a new viewport. In this article, we formulate this as a perceptually optimized quality adaptation problem to maximize the Quality of Experience (QoE) for the refinement from a LQ scale to another HQ level within a specific duration under the given network constraint. With our developed perceptual model considering the adaptation quality for VDS of omnidirectional videos, we first provide baseline solutions numerically, demonstrating the noticeable subjective improvements of model-driven solution against the heuristic selection based approach. We also propose a novel viewport prediction algorithm based on the Hidden Markov Model (HMM), and experimental results show that it significantly outperforms the relevant methods with better prediction accuracy. We then improve the adaptation strategy with proposed viewport prediction-based data prefetching, leading to better visual perception than the baseline system at the same bandwidth constraint. Generally, prefetching the content of predicted next viewport one second ahead of its playback time, would lead to more than 8% Bjontegaard Delta Rate (BD-Rate) gain.

中文翻译：

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视口相关的全向视频流的感知优化质量适应

Viewport-Dependent Streaming (VDS) 是实践中交付全向视频的首选方式，其中对当前视口中的内容应用高质量 (HQ) 比例，但在其他地方应用低质量 (LQ) 比例。在用户稳定对新视口的注视后，会发生质量适应或改进。在本文中，我们将其表述为感知优化的质量适应问题，以最大限度地提高体验质量 (QoE)，以便在给定网络约束下的特定持续时间内从 LQ 规模细化到另一个 HQ 级别。我们开发的感知模型考虑了全向视频的 VDS 的适应质量，我们首先以数值方式提供基线解决方案，展示了模型驱动解决方案相对于基于启发式选择的方法的显着主观改进。我们还提出了一种基于隐马尔可夫模型（HMM）的新视口预测算法，实验结果表明，它以更好的预测精度显着优于相关方法。然后，我们通过提出的基于视口预测的数据预取来改进适应策略，从而在相同的带宽约束下获得比基线系统更好的视觉感知。通常，在播放时间前一秒预取预测的下一个视口的内容，将导致超过 8% 的 Bjontegaard Delta Rate (BD-Rate) 增益。然后，我们通过提出的基于视口预测的数据预取来改进适应策略，从而在相同的带宽约束下获得比基线系统更好的视觉感知。通常，在播放时间前一秒预取预测的下一个视口的内容，将导致超过 8% 的 Bjontegaard Delta Rate (BD-Rate) 增益。然后，我们通过提出的基于视口预测的数据预取来改进适应策略，从而在相同的带宽约束下获得比基线系统更好的视觉感知。通常，在播放时间前一秒预取预测的下一个视口的内容，将导致超过 8% 的 Bjontegaard Delta Rate (BD-Rate) 增益。