论文标题
使用深信念网络的量化数据对压缩视频的多帧质量增强
Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks
论文作者
论文摘要
在流媒体和监视的时代，压缩视频增强已成为需要不断改进的问题。在这里，我们研究了一种改善多帧质量增强方法的方法。这种方法包括利用该地区具有峰值质量的框架来改善该地区质量较低的框架。这种方法包括使用深度信念网络从视频中获取量化数据。然后将量化的数据馈入MF-CNN体系结构，以改善压缩视频。我们进一步研究了使用BI-LSTM检测峰质量帧的影响。我们的方法比使用SVM进行PQF检测的MFQE的第一种方法获得了更好的结果。另一方面，我们的MFQE方法并不能胜过最新版本的MQFE方法，该方法使用Bi-LSTM进行PQF检测。
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.
