  In this paper, we proposed a no-reference (NR) quality metric for RGB plus
image-depth (RGB-D) synthesis images based on Generative Adversarial Networks
(GANs), namely GANs-NQM. Due to the failure of the inpainting on dis-occluded
regions in RGB-D synthesis process, to capture the non-uniformly distributed
local distortions and to learn their impact on perceptual quality are
challenging tasks for objective quality metrics. In our study, based on the
characteristics of GANs, we proposed i) a novel training strategy of GANs for
RGB-D synthesis images using existing large-scale computer vision datasets
rather than RGB-D dataset; ii) a referenceless quality metric based on the
trained discriminator by learning a `Bag of Distortion Word' (BDW) codebook and
a local distortion regions selector; iii) a hole filling inpainter, i.e., the
generator of the trained GANs, for RGB-D dis-occluded regions as a side
outcome. According to the experimental results on IRCCyN/IVC DIBR database, the
proposed model outperforms the state-of-the-art quality metrics, in addition,
is more applicable in real scenarios. The corresponding context inpainter also
shows appealing results over other inpainting algorithms.
