The diffusion model is a new type of generative model. In this work, We propose a generative video quality enhancement method based on the diffusion model. Compared with the existing methods, our method requires only one model to improve the quality of videos encoded with different quantization parameters, and the structure of this model is shown in Figure 1 .

In this work, we propose a low-complexity convolution neural network for compressed video post-processing. The main process can be expressed as follows:

In this paper, we propose an efficient convolution neural network to enhance the quality of video compressed by HEVC standard. The model is composed of a max-pooling module and a hierarchical feature fusion module. The max-pooling module extracts feature from different scales and enlarges the receptive field of the model without stacking too many convolution layers. And the hierarchical feature fusion module accurately aligns features from different scales and fuses them efficiently. Two modules are applied in the proposed network, our model reconstructs compressed video frames with higher visual quality. Besides, the model is constructed in the full convolution network, thus it can adapt to videos in variable resolutions. The experiment results show that the proposed model outperforms existing models in the terms of PSNR under the same dataset.

Multivariate time series (MTS) prediction aims at predicting future time series by extracting multiple forms of dependencies of past time series. Traditional prediction methods and deep learning-based prediction methods focus on extracting the dynamic relationships of certain aspects of MTS, especially the temporal characteristics, often neglecting the spatial and temporal dynamic correlations of MTS. Inspired by convolution neural network (CNN) and attention mechanism, this paper proposes a convolution LSTM network model based on MTS prediction with two-stage attention. Specifically, we first propose a new MTS preprocessing method to perform convolution operations better. Then convolution layer extracts spatial correlation of MTS and LSTM model extracts temporal correlation. It is worth mentioning that the combination of attention mechanism and LSTM can effectively solve the problem of insufficient time dependency in MTS prediction. In addition, dual-stage attention mechanism can effectively eliminate irrelevant information, select the relevant exogenous sequence, give it higher weight, and increase the past value of the target sequence to further eliminate irrelevant information. Finally, the MTS spatio-temporal correlation is extracted to improve the prediction accuracy, and the model is interpreted. Experimental results show that the model has broad application prospects. Experiments based on typical datasets of finance, environment, and energy determine the optimal window size and hidden size of the prediction, and demonstrate that the model achieves the state-of-the-art effect compared to the other four deep learning models. On top of that, the model is not only suitable for single-step prediction of MTS, but also suitable for multistep prediction of time step in a certain range.

In a video conversation, the participants usually remain the same. As the conversation continues, similar facial expressions of the same person would occur intermittently. However, the correlation of similar face features has not been fully used since the conventional methods only focus on independent frames. We set up a face feature database and updated it online to include new facial expressions during the whole conversation. At the receiver side, the database is used to recover the face distortion and thus improve the visual quality. Additionally, the proposed method brings small burden to update the database and is generic to various CODEC.