A Comparative Study of Algorithms for Realtime Panoramic Video Blending

TL;DR

This paper compares six algorithms for real-time panoramic video blending, evaluating their performance, quality, and resource usage to guide virtual reality applications.

Contribution

It provides a comprehensive comparison of six popular video blending algorithms specifically for real-time panoramic video, including performance benchmarks and quality assessments.

Findings

Multi-band blending offers high visual quality.

Convolution pyramid blending is computationally efficient.

GPU implementations significantly improve processing speed.

Abstract

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 in particular realtime panoramic video blending, a key problem in various virtual reality tasks. To evaluate the performance of the 6 algorithms on this problem, we have created a video benchmark of 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 available.