Personalized Dual-Level Color Grading for 360-degree Images in Virtual Reality.

The rising popularity of 360-degree images and virtual reality (VR) has spurred a growing interest among creators in producing visually appealing content through effective color grading processes. Although existing computational approaches have simplified the global color adjustment for entire images with Preferential Bayesian Optimization (PBO), they neglect local colors for points of interest and are not optimized for the immersive nature of VR. In response, we propose a dual-level PBO framework that integrates global and local color adjustments tailored for VR environments. We design and evaluate a novel context-aware preferential Gaussian Process (GP) to learn contextual preferences for local colors, taking into account the dynamic contexts of previously established global colors. Additionally, recognizing the limitations of desktop-based interfaces for comparing 360-degree images, we design three VR interfaces for color comparison. We conduct a controlled user study to investigate the effectiveness of the three VR interface designs and find that users prefer to be enveloped by one 360-degree image at a time and to compare two rather than four color-graded options.