A method of video decoding for a video decoder, the method comprising: receiving a video bitstream corresponding to one or more pictures in a video sequence; decoding each picture using a decoding process comprising one or a combination of a residual decoding process to generate reconstructed residual from the video bitstream, a prediction process to generate a prediction signal related to each picture, a reconstruction process to generate a reconstructed picture from the reconstructed residual and the prediction signal, and one or more filtering processes comprising a deblocking filter , a Sample Adaptive Offset, SAO , an Adaptive Loop Filter, ALF , or any combination thereof applied to the reconstructed picture, characterized by the method further comprising: processing an output of the reconstruction process as a target signal (D) using a Deep Neural Network, DNN, and providing output data of the DNN as an input to a filtering process of the one or more filtering processes ; and/or processing an output of a first filtering process of the one or more filtering processes as a target signal (C, B) using a DNN, and providing output data of the DNN as an input to a second filtering process of the one or more filtering processes ; and/or processing an output of a filtering process of the one or more filtering processes as a target signal (A) using a DNN, and providing output data of the DNN as decoded picture. receiving a video bitstream corresponding to one or more pictures in a video sequence; decoding each picture using a decoding process comprising one or a combination of a residual decoding process to generate reconstructed residual from the video bitstream, a prediction process to generate a prediction signal related to each picture, a reconstruction process to generate a reconstructed picture from the reconstructed residual and the prediction signal, and one or more filtering processes comprising a deblocking filter , a Sample Adaptive Offset, SAO , an Adaptive Loop Filter, ALF , or any combination thereof applied to the reconstructed picture, characterized by the method further comprising: processing an output of the reconstruction process as a target signal (D) using a Deep Neural Network, DNN, and providing output data of the DNN as an input to a filtering process of the one or more filtering processes ; and/or processing an output of a first filtering process of the one or more filtering processes as a target signal (C, B) using a DNN, and providing output data of the DNN as an input to a second filtering process of the one or more filtering processes ; and/or processing an output of a filtering process of the one or more filtering processes as a target signal (A) using a DNN, and providing output data of the DNN as decoded picture. processing an output of the reconstruction process as a target signal (D) using a Deep Neural Network, DNN, and providing output data of the DNN as an input to a filtering process of the one or more filtering processes ; and/or processing an output of a first filtering process of the one or more filtering processes as a target signal (C, B) using a DNN, and providing output data of the DNN as an input to a second filtering process of the one or more filtering processes ; and/or processing an output of a filtering process of the one or more filtering processes as a target signal (A) using a DNN, and providing output data of the DNN as decoded picture. <EOS>