Objective image quality assessment of 3D synthesized views

Depth-Image-Based-Rendering (DIBR) techniques are essential for three-dimensional (3D) video applications such as 3D Television (3DTV) and Free-Viewpoint Video. However, this process is based on 3D warping and can induce serious distortions whose impact on the perceived quality is far different from the one experienced in the 2D imaging processes. Since quality evaluation of DIBR-synthesized views is fundamental for the design of perceptually friendly 3D video systems, an appropriate objective quality metric targeting the assessment of DIBR-synthesized views is momentous. Most of the 2D objective quality metrics fail in assessing the visual quality of DIBR-synthesized views because they have not been conceived for addressing the specificities of DIBR-related distortions. In this paper, a new full-reference objective quality metric, 3DSwIM (3D Synthesized view Image Quality Metric), dedicated to artifacts detection in DIBR-synthesized view-points is presented. The proposed scheme relies on a comparison of statistical features of wavelet subbands of two input images: the original image and the DIBR-based synthesized image. A registration step is included before the comparison step so that best matching blocks are always compared to ensure "shifting-resilience". In addition, a skin detection step weights the final quality score in order to penalize distorted blocks containing "skin-pixels" based on the assumption that a human observer is most sensitive to impairments affecting human subjects. Experimental tests show that the proposed method outperforms the conventional 2D and DIBR-dedicated quality metrics under test. HighlightsThis paper presents a new full- reference objective quality metric dedicated to artifacts detection in 3D synthesized views.The proposed metric is based on the comparison of statistical features of wavelet subbands of the original image and the DIBR-based synthesized image."Shifting-resilience" is granted by the use of a registration algorithm.The final quality score is weighted depending on the presence of "skin-pixels" based on the assumption that a human observer is more sensitive to impairments affecting human subjects.Experimental tests show that the proposed method outperforms the 2D conventional and DIBR-synthesized views dedicated quality metrics under test.