The rise in popularity of three-dimensional video has led to an extensive usage of depth image-based rendering (DIBR) technology in entertainment, military, education, and other fields. Image quality assessment of DIBR technology has received a lot of attention in recent years because it is one of the main technologies of virtual viewpoint synthesis. The quality of the synthesized image and video is critical to the successful application of the associated technology. Thus, based on the statistical features, this paper proposes a no-reference quality assessment model for DIBR images. First, the texture distortions of DIBR images are detected by Benford's law, and then variation of discrete cosine transform (DCT) coefficients and the statistical characteristics of the natural scene are extracted. Finally, the prediction scores are obtained by training the extracted features using support vector regression (SVR). The proposed method is highly consistent with human subjective evaluation, according to the experimental results on three public image datasets: IVC, IETR, and MCL-3D.