Estimating Scene-Oriented Pseudo Depth With Pictorial Depth Cues

Abstract

Estimating depth information from a single image has recently attracted great attention in 3D-TV applications, such as 2D-to-3D conversion owing to an insufficient supply of 3-D contents. In this paper, we present a new framework for estimating depth from a single image via scene classification techniques. Our goal is to produce perceptually reasonable depth for human viewers; we refer to this as pesudo depth estimation. Since the human visual system highly relies on structural information and salient objects in understanding scenes, we propose a framework that combines two depth maps: initial pseudo depth map (PDM) and focus depth map. We use machine learning based scene classification to classify the image into one of two classes, namely, object-view and non-object-view. The initial PDM is estimated by segmenting salient objects (in the case of object-view) and by analyzing scene structures (in the case of non-object-view). The focus blur is locally measured to improve the initial PDM. Two depth maps are combined, and a simple filtering method is employed to generate the final PDM. Simulation results show that the proposed method outperforms other state-of-the-art approaches for depth estimation in 2D-to-3D conversion, both quantitatively and qualitatively. Furthermore, we discuss how the proposed method can effectively be extended to image sequences by employing depth propagation techniques.