Quality evaluation of depth map error concealment using a perceptually-aware objective metric

This paper presents a quality evaluation study on the performance of error concealment methods for depth maps used in multiview video-plus-depth (MVD). The research deals with the problem of decoding corrupted depth maps received from error-prone networks, where the quality of the reconstructed depth data is not always directly related to the quality of the virtual views synthesised by those maps. Even after error concealment such distortions are not particularly perceived as other known types, such as coding distortion. Thus, traditional quality metrics are not adequate to capture all the relevant features. In this work, the performance of two error concealment methods for depth maps is evaluated using a perceptually-aware objective metric. This metric is validated through subjective assessment of virtual views synthesised with concealed depth maps. Each subjective test is performed by comparing the relative quality between between two synthesised images using different error concealment methods. The perceptual impact of reconstruction in corrupted depth of MVD is evaluated under various loss rates, using several colour images and depth maps encoded at multiple quantisation steps. The achieved results reveal that the proposed objective quality metric is mostly inline with user preferences, in respect to the relative performance of each error concealment method.