Quality assessment of multi-view-plus-depth images
Multi-view-plus-depth (MVD) representation has gained significant attention recently as a means to encode 3D scenes, allowing for intermediate views to be synthesized on-the-fly at the display site through depth-image-based-rendering (DIBR). Automatic quality assessment of MVD images/videos is critical for the optimal design of MVD image/video coding and transmission schemes. Most existing image quality assessment (IQA) and video quality assessment (VQA) methods are applicable only after the DIBR process. Such post-DIBR measures are valuable in assessing the overall system performance, but are difficult to be directly employed in the encoder optimization process in MVD image/video coding. Here we make one of the first attempts to develop a perceptual pre-DIBR IQA approach for MVD images by employing an information content weighted approach that balances between local quality measures of texture and depth images. Experiment results show that the proposed approach achieves competitive performance when compared with state-of-the-art IQA algorithms applied post-DIBR.