Visual Quality Assessment for Projected Content

Abstract

Today’s projectors are widely used for information and media display in a stationary setup. There is also a growing effort to deploy projectors creatively, such as using a mobile projector to display visual content on an arbitrary surface. However, the quality of projected content is often limited by the quality of projection surface, environment lighting, and nonoptimal projector settings. This paper presents a visual quality assessment method for projected content. Our method assesses the quality of the projected image by analyzing the projected image captured by a camera. The key challenge is that the quality of the captured image is often different from the perceived quality by a viewer as she “sees” the projected image differently than the camera. To address this problem, our method employs a data-driven approach that learns from the labeled data to bridge this gap. Our method integrates both manually crafted features and deep learning features and formulates projection quality assessment as a regression problem. Our experiments on a wide range of projection content, projection surfaces, and environment lighting show that our method can reliably score the quality of projected visual content in a way that is consistent with the human perception.