  In recent years, neural network-based image compression techniques have been
able to outperform traditional codecs and have opened the gates for the
development of learning-based video codecs. However, to take advantage of the
high temporal correlation in videos, more sophisticated architectures need to
be employed. This paper presents PredEncoder, a hybrid video compression
framework based on the concept of predictive auto-encoding that models the
temporal correlations between consecutive video frames using a prediction
network which is then combined with a progressive encoder network to exploit
the spatial redundancies. A variable-rate block encoding scheme has been
proposed in the paper that leads to remarkably high quality to bit-rate ratios.
By joint training and fine-tuning of this hybrid architecture, PredEncoder has
been able to gain significant improvement over the MPEG-4 codec and has
achieved bit-rate savings over the H.264 codec in the low to medium bit-rate
range for HD videos and comparable results over most bit-rates for non-HD
videos. This paper serves to demonstrate how neural architectures can be
leveraged to perform at par with the highly optimized traditional methodologies
in the video compression domain.
