Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks

TL;DR

This paper proposes an enhanced multi-frame quality enhancement method for compressed videos using deep belief networks and investigates the impact of Bi-LSTM for peak quality frame detection, showing improved results over some prior methods.

Contribution

It introduces a novel approach combining deep belief networks and Bi-LSTM for improved peak quality frame detection in video enhancement.

Findings

Outperforms the original MFQE with SVM-based PQF detection.

Does not outperform the latest MQFE with Bi-LSTM detection.

Shows improved quality enhancement results over initial MFQE methods.

Abstract

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.