Viewing omnidirectional images (ODIs) in virtual reality (VR) represents a novel form of media that provides immersive experiences for users to navigate and interact with digital content. Nonetheless, this sense of immersion can be greatly compromised by a blur effect that masks details and hampers the user's ability to engage with objects of interest. In this paper, we present a novel system, called OmniVR, designed to enhance visual clarity during VR navigation. Our system enables users to effortlessly locate and zoom in on the objects of interest in VR. It captures user commands for navigation and zoom, converting these inputs into parameters for the Mobius transformation matrix. Leveraging these parameters, the ODI is refined using a learning-based algorithm. The resultant ODI is presented within the VR media, effectively reducing blur and increasing user engagement. To verify the effectiveness of our system, we first evaluate our algorithm with state-of-the-art methods on public datasets, which achieves the best performance. Furthermore, we undertake a comprehensive user study to evaluate viewer experiences across diverse scenarios and to gather their qualitative feedback from multiple perspectives. The outcomes reveal that our system enhances user engagement by improving the viewers' recognition, reducing discomfort, and improving the overall immersive experience. Our system makes the navigation and zoom more user-friendly.

翻译：在虚拟现实中观看全景图像（ODI）代表了一种新颖的媒体形式，为用户提供沉浸式导航和交互数字内容的体验。然而，这种沉浸感常因模糊效应而大打折扣，该效应掩盖细节并阻碍用户与感兴趣对象交互。本文提出名为OmniVR的新系统，旨在增强VR导航过程中的视觉清晰度。该系统使用户能够轻松定位并放大VR中的感兴趣对象。它捕捉用户的导航和缩放指令，并将这些输入转换为莫比乌斯变换矩阵的参数。利用这些参数，采用基于学习的算法对ODI进行优化。生成的ODI在VR媒体中呈现，有效减少模糊并提升用户参与度。为验证系统有效性，我们首先在公共数据集上评估算法与现有最先进方法，获得了最佳性能。此外，我们开展全面的用户研究，评估不同场景下的观看体验，并从多个维度收集定性反馈。结果显示，我们的系统通过提升用户识别能力、减少不适感并改善整体沉浸体验来增强用户参与度。该系统使导航与缩放更加用户友好。