Visual Quality Assessment for Projected Content

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 non-optimal 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.