Despite substantial efforts dedicated to the design of heuristic models for
omnidirectional (i.e., 360{\deg}) image quality assessment (OIQA), a
conspicuous gap remains due to the lack of consideration for the diversity of
viewing behaviors that leads to the varying perceptual quality of 360{\deg}
images. Two critical aspects underline this oversight: the neglect of viewing
conditions that significantly sway user gaze patterns and the overreliance on a
single viewport sequence from the 360{\deg} image for quality inference. To
address these issues, we introduce a unique generative scanpath representation
(GSR) for effective quality inference of 360{\deg} images, which aggregates
varied perceptual experiences of multi-hypothesis users under a predefined
viewing condition. More specifically, given a viewing condition characterized
by the starting point of viewing and exploration time, a set of scanpaths
consisting of dynamic visual fixations can be produced using an apt scanpath
generator. Following this vein, we use the scanpaths to convert the 360{\deg}
image into the unique GSR, which provides a global overview of gazed-focused
contents derived from scanpaths. As such, the quality inference of the
360{\deg} image is swiftly transformed to that of GSR. We then propose an
efficient OIQA computational framework by learning the quality maps of GSR.
Comprehensive experimental results validate that the predictions of the
proposed framework are highly consistent with human perception in the
spatiotemporal domain, especially in the challenging context of locally
distorted 360{\deg} images under varied viewing conditions. The code will be
released at this https URL

通过引入独特的生成式扫描路径表示法（GSR），针对 360 度图像，本研究提出了一种有效的质量推断方法，聚合了多假设用户在预定义的视角条件下的多样的感知经验，从而将 360 度图像的质量推断迅速转化为 GSR 的质量推断，该方法在各种观看条件下，特别是在局部失真的挑战性背景下，与人类感知在时空领域高度一致。

基于生成扫描路径表示的 360° 图像感知质量评估

Perceptual Quality Assessment of 360° Images Based on Generative  Scanpath Representation

Manually re-drawing an image in a certain artistic style takes a professional
artist a long time. Doing this for a video sequence single-handedly is beyond
imagination. We present two computational approaches that transfer the style
from one image (for example, a painting) to a whole video sequence. In our
first approach, we adapt to videos the original image style transfer technique
by Gatys et al. based on energy minimization. We introduce new ways of
initialization and new loss functions to generate consistent and stable
stylized video sequences even in cases with large motion and strong occlusion.
Our second approach formulates video stylization as a learning problem. We
propose a deep network architecture and training procedures that allow us to
stylize arbitrary-length videos in a consistent and stable way, and nearly in
real time. We show that the proposed methods clearly outperform simpler
baselines both qualitatively and quantitatively. Finally, we propose a way to
adapt these approaches also to 360 degree images and videos as they emerge with
recent virtual reality hardware.

本文提出了两种计算机视觉算法对图像和视频进行风格迁移，第一种是根据 Gatys 算法进行能量最小化的图像风格转移技术，针对图像中的规模和遮挡等问题提出了改进，第二种是将视频风格迁移建模为一个学习问题，使用深度学习网络和新的训练程序使其在几乎实时的情况下实现任意长度的视频稳定风格转移，最后还介绍了如何将这些方法应用于 360 度图像和视频。