  The introduction of multiple viewpoints in video scenes inevitably increases
the bitrates required for storage and transmission. To reduce bitrates,
researchers have developed methods to skip intermediate viewpoints during
compression and delivery, and ultimately reconstruct them using Side
Information (SI). Typically, depth maps are used to construct SI. However,
their methods suffer from inaccuracies in reconstruction and inherently high
bitrates. In this paper, we propose a novel multi-view video coding method that
leverages the image generation capabilities of Generative Adversarial Network
(GAN) to improve the reconstruction accuracy of SI. Additionally, we consider
incorporating information from adjacent temporal and spatial viewpoints to
further reduce SI redundancy. At the encoder, we construct a spatio-temporal
Epipolar Plane Image (EPI) and further utilize a convolutional network to
extract the latent code of a GAN as SI. At the decoder side, we combine the SI
and adjacent viewpoints to reconstruct intermediate views using the GAN
generator. Specifically, we establish a joint encoder constraint for
reconstruction cost and SI entropy to achieve an optimal trade-off between
reconstruction quality and bitrates overhead. Experiments demonstrate
significantly improved Rate-Distortion (RD) performance compared with
state-of-the-art methods.
