Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks
This work addresses video quality improvement for streaming and surveillance applications, but it is incremental as it builds on existing multi-frame enhancement approaches.
The paper tackles compressed video enhancement by proposing a multi-frame quality enhancement method that uses quantized data from a deep belief network and a Bi-LSTM for peak quality frame detection, achieving better results than an SVM-based baseline but not outperforming the latest Bi-LSTM version.
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.