  Single-image-based view generation (SIVG) is important for producing 3D
stereoscopic content. Here, handling different spatial resolutions as input and
optimizing both reconstruction accuracy and processing speed is desirable.
Latest approaches are based on convolutional neural network (CNN), and they
generate promising results. However, their use of fully connected layers as
well as pre-trained VGG forces a compromise between reconstruction accuracy and
processing speed. In addition, this approach is limited to the use of a
specific spatial resolution. To remedy these problems, we propose exploiting
fully convolutional networks (FCN) for SIVG. We present two FCN architectures
for SIVG. The first one is based on combination of an FCN and a view-rendering
network called DeepView$_{ren}$. The second one consists of decoupled networks
for luminance and chrominance signals, denoted by DeepView$_{dec}$. To train
our solutions we present a large dataset of 2M stereoscopic images. Results
show that both of our architectures improve accuracy and speed over the state
of the art. DeepView$_{ren}$ generates competitive accuracy to the state of the
art, however, with the fastest processing speed of all. That is x5 times faster
speed and x24 times lower memory consumption compared to the state of the art.
DeepView$_{dec}$ has much higher accuracy, but with x2.5 times faster speed and
x12 times lower memory consumption. We evaluated our approach with both
objective and subjective studies.
