ODVista：一个面向超分辨率与质量增强任务的全向视频数据集
ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks
AI总结：
针对现有公开360度视频超分辨率数据集缺乏压缩伪影的问题，本文提出包含200段经HEVC/H.265编码的高分辨率视频的ODVista数据集，并评估了四类超分辨率模型在该数据集上的质量增强与运行性能。
摘要：
全向（即360度）视频的应用正日益广泛，这主要得益于沉浸式虚拟现实（VR）和扩展现实（XR）技术的最新进展。然而，这类视频在流媒体传输中会遇到带宽和延迟相关的挑战，在无人机（UAV）等移动场景下尤为突出。自适应分辨率与压缩技术旨在在这些约束下保持低延迟的同时维持画质，但降尺度与编码过程仍会降低画质并引入伪影。基于机器学习（ML）的超分辨率（SR）与质量增强技术能够提升细节恢复效果并减少压缩伪影，是一种颇具前景的解决方案。但目前公开的360度视频超分辨率数据集均不含压缩伪影，这限制了该领域的研究。为填补这一空白，本文推出全向视频流媒体数据集ODVista，该数据集包含200段高分辨率高质量视频，均采用高效视频编码（HEVC）/H.265标准在四个比特率区间下完成降尺度与编码处理。评估结果显示，该数据集不仅场景类型丰富，还覆盖了不同层级的内容复杂度，这对于研发在真实场景中表现优异、且能在多样视觉环境下具备泛化能力的鲁棒解决方案至关重要。此外，我们还在ODVista的验证集与测试集上，从质量增强效果与运行时间两方面评估了两种手工设计的SR模型与两种基于ML的SR模型的性能。
英文摘要：
Omnidirectional or 360-degree video is being increasingly deployed, largely due to the latest advancements in immersive virtual reality (VR) and extended reality (XR) technology. However, the adoption of these videos in streaming encounters challenges related to bandwidth and latency, particularly in mobility conditions such as with unmanned aerial vehicles (UAVs). Adaptive resolution and compression aim to preserve quality while maintaining low latency under these constraints, yet downscaling and encoding can still degrade quality and introduce artifacts. Machine learning (ML)-based super-resolution (SR) and quality enhancement techniques offer a promising solution by enhancing detail recovery and reducing compression artifacts. However, current publicly available 360-degree video SR datasets lack compression artifacts, which limit research in this field. To bridge this gap, this paper introduces omnidirectional video streaming dataset (ODVista), which comprises 200 high-resolution and high quality videos downscaled and encoded at four bitrate ranges using the high-efficiency video coding (HEVC)/H.265 standard. Evaluations show that the dataset not only features a wide variety of scenes but also spans different levels of content complexity, which is crucial for robust solutions that perform well in real-world scenarios and generalize across diverse visual environments. Additionally, we evaluate the performance, considering both quality enhancement and runtime, of two handcrafted and two ML-based SR models on the validation and testing sets of ODVista.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
