Zhu Zhe, Lu Jiaming, Wang Minxuan, Zhang Songhai, Martin Ralph R, Liu Hantao, Hu Shi-Min
IEEE Trans Image Process. 2018 Jun;27(6):2952-2965. doi: 10.1109/TIP.2018.2808766. Epub 2018 Feb 22.
Unlike image blending algorithms, video blending algorithms have been little studied. In this paper, we investigate 6 popular blending algorithms-feather blending, multi-band blending, modified Poisson blending, mean value coordinate blending, multi-spline blending and convolution pyramid blending. We consider their application to blending realtime panoramic videos, a key problem in various virtual reality tasks. To evaluate the performances and suitabilities of the 6 algorithms for this problem, we have created a video benchmark with several videos captured under various conditions. We analyze the time and memory needed by the above 6 algorithms, for both CPU and GPU implementations (where readily parallelizable). The visual quality provided by these algorithms is also evaluated both objectively and subjectively. The video benchmark and algorithm implementations are publicly available1.
与图像融合算法不同,视频融合算法的研究较少。在本文中,我们研究了6种流行的融合算法——羽化融合、多波段融合、改进的泊松融合、均值坐标融合、多样条融合和卷积金字塔融合。我们考虑将它们应用于实时全景视频融合,这是各种虚拟现实任务中的一个关键问题。为了评估这6种算法在该问题上的性能和适用性,我们创建了一个视频基准测试,其中包含在各种条件下拍摄的多个视频。我们分析了上述6种算法在CPU和GPU实现(易于并行化的情况)下所需的时间和内存。还从客观和主观两方面对这些算法提供的视觉质量进行了评估。视频基准测试和算法实现可公开获取。