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.

翻译：在流流和监视时代,压缩视频增强已成为需要不断改进的问题。 在这里, 我们调查一种改进多框架质量提高方法的方法。 这种方法包括利用该地区最高质量的框架来改善该地区低质量的图像。 这种方法包括利用深层信仰网络从视频中获得量化数据。 然后, 将量化数据输入MF- CNN结构, 以改善压缩视频。 我们进一步调查使用Bi- LSTM来检测最高质量框架的影响。 我们的方法比MFQE的第一种方法(使用SVM来检测PQF)取得更好的效果。 另一方面, 我们的MFQE方法并不超越使用Bi- LSTM来检测PQF的最新版本的MQF方法。