论文标题
使用基于深度学习的预测模型进行视频质量增强，以量化MPEG I框架中的DCT系数
Video Quality Enhancement Using Deep Learning-Based Prediction Models for Quantized DCT Coefficients in MPEG I-frames
论文作者
论文摘要
最近的工作成功地应用了某些类型的卷积神经网络（CNN），以减少因损失的JPEG/MPEG压缩技术而引起的明显失真。它们中的大多数建立在空间域上进行的处理。在这项工作中，我们提出了一个纯粹基于频到频率域的MPEG视频解码器：它读取从低质量的I-Frames bitstream中收到的量化DCT系数，并使用基于深度学习的模型来预测缺失的系数，以使相同框架具有增强质量的相同帧。在使用视频数据集的实验中，我们的最佳模型能够从具有量化的DCT系数（QF）的量子框架（QF）为10的框架，以增强质量框架，QF略接近20。
Recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG/MPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a MPEG video decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCT coefficients received from a low-quality I-frames bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same frames with enhanced quality. In experiments with a video dataset, our best model was able to improve from frames with quantized DCT coefficients corresponding to a Quality Factor (QF) of 10 to enhanced quality frames with QF slightly near to 20.
