3D Visual Comfort Assessment via Sparse Coding

The issue of visual discomfort has long been restricting the development of advanced stereoscopic 3D video technology. Bolstered by the requirement of highly comfortable three-dimensional (3D) content service, predicting the degree of visual comfort automatically with high accuracy has become a topic of intense study. This paper presents a novel visual comfort assessment (VCA) metric based on sparse coding strategy. The proposed VCA metric comprises three stages: feature representation, dictionary construction, sparse coding, and pooling strategy, respectively. In the feature representation stage, visual saliency labeled disparity statistics and neural activities are computed to capture the overall degree of visual comfort for a certain stereoscopic image. A set of stereoscopic images with a wide range degree of visual comfort are selected to construct dictionary for sparse coding. Given an input stereoscopic image, by representing features in the constructed dictionary via sparse coding algorithm, the corresponding visual comfort score can be estimated by weighting mean opinion scores (MOSs) using the sparse coding coefficients. In addition, we conduct a new 3D image benchmark database for performance validation. Experimental results on this database demonstrate that the proposed metric outperforms some representative VCA metrics in the regard of consisting with human subjective judgment.