No-reference quality metric for depth maps

The performances of several 3D imaging/video applications (going from 3DTV to video surveillance) benefit from the estimation or acquisition of accurate and high quality depth maps. However, the characteristics of depth information is strongly affected by the procedure employed in its acquisition or estimation (e.g., stereo evaluation, ToF cameras, structured light sensors, etc.), and the very definition of “quality” for a depth map is still under investigation. In this paper we proposed an unsupervised quality metric for depth information in Depth Image Based Rendering signals that predicts the accuracy in synthesizing 3D models and lateral views by using the considered depth information. The metric has been tested on depth maps generate with different algorithms and sensors. Moreover, experimental results show how it is possible to progressively improve the performance of 3D modelization by controlling the device/algorithm with this metric.