Interpolation error as a quality metric for stereo: Robust, or not?

To properly benchmark and stimulate current stereo algorithms specifically in the application context of view interpolation, a robust quantitative evaluation approach is important. As a prevailing quality assessment method, interpolation error has been widely used. It measures the distortions between an interpolated image and a real camera image for a desired virtual viewpoint. However, is it a robust quality metric, especially when state-of-the-art stereo technology is developing so fast? This paper hence focuses on revealing several rarely attended weaknesses that make the interpolation error evaluation paradigm vulnerable. In addition, we propose an alternative evaluation method as an early attempt at addressing these challenges, from a perspective of communication system. Evaluation of representative stereo methods from the Middlebury Web site shows that the new approach yields consistent quality assessment outcomes.