No-reference quality assessment for depth image based rendering-synthesized images based on multi-feature fusion

The depth image based rendering (DIBR) technique plays a crucial role in free viewpoint video. However, the DIBR technology is not yet mature and still needs to be improved. In addition, depth image acquisition is inaccurate and prone to distortion, which affects human visual perception. The DIBR-synthesized image quality assessment (IQA) method, which simulates the human visual perception system, can be used as feedback to improve the DIBR technique and depth video quality. The existing IQA algorithms are insensitive to the diversity of geometric distortion produced by the DIBR technique and do not consider the impact of geometric distortion on the image quality from multiple perspectives. In this work, a no-reference IQA method for DIBR-synthesized images based on multi-feature fusion is proposed. Different from the existing IQA algorithms, this paper focuses on the diversity of geometric distortions in DIBR-synthesized images, measures the distortions of the edge region and the hole region, and integrates the global sharpness and the natural mixed distortion. Specifically, the global sharpness is first measured, and the image is divided into blocks to further improve the accuracy of the results. Secondly, the geometric distortions of the edge and hole regions are measured comprehensively. In addition, considering the impact of the natural mixed distortion on the quality of the DIBR-synthesized image, a Gaussian mixed statistical model is used to extract the natural mixed distortion of the DIBR-synthesized image. Finally, the quality score of the DIBR-synthesized image is generated by pooling regression. Experimental results on two synthesized image databases demonstrate that the method proposed in this paper outperforms classical and state-of-the-art IQA methods and is more aligned with the human visual perception system.