  Inter-Prediction is used effectively in multiple standards, including H.264
and HEVC (also known as H.265). It leverages correlation between blocks of
consecutive video frames in order to perform motion compensation and thus
predict block pixel values and reduce transmission bandwidth. In order to
reduce the magnitude of the transmitted Motion Vector (MV) and thus reduce
bandwidth, the encoder utilizes Predicted Motion Vector (PMV), which is derived
by taking the median vector of the corresponding MVs of the neighboring blocks.
In this research, we propose innovative methods, based on neural networks
prediction, for improving the accuracy of the calculated PMV. We begin by
showing a straightforward approach of calculating the best matching PMV and
signaling its neighbor block index value to the decoder while reducing the
number of bits required to represent the result without adding any computation
complexity. Then we use a classification Fully Connected Neural Networks (FCNN)
to estimate from neighbors the PMV without requiring signaling and show the
advantage of the approach when employed for high motion movies. We demonstrate
the advantages using fast forward movies. However, the same improvements apply
to camera streams of autonomous vehicles, drone cameras, Pan-Tilt-Zoom (PTZ)
cameras, and similar applications whereas the MVs magnitudes are expected to be
large. We also introduce a regression FCNN to predict the PMV. We calculate
Huffman coded streams and demonstrate an order of ~34% reduction in number of
bits required to transmit the best matching calculated PMV without reducing the
quality, for fast forward movies with high motion.
