  In the age of streaming and surveillance compressed video enhancement has
become a problem in need of constant improvement. Here, we investigate a way of
improving the Multi-Frame Quality Enhancement approach. This approach consists
of making use of the frames that have the peak quality in the region to improve
those that have a lower quality in that region. This approach consists of
obtaining quantized data from the videos using a deep belief network. The
quantized data is then fed into the MF-CNN architecture to improve the
compressed video. We further investigate the impact of using a Bi-LSTM for
detecting the peak quality frames. Our approach obtains better results than the
first approach of the MFQE which uses an SVM for PQF detection. On the other
hand, our MFQE approach does not outperform the latest version of the MQFE
approach that uses a Bi-LSTM for PQF detection.
