APPAREIL ET PROCÉDÉ DE FILTRAGE AVEC APPRENTISSAGE EN PROFONDEUR EN FONCTION DU MODE

摘要

Deep learning may be used in video compression for in-loop filtering in order to reduce artifacts. To improve the performance of a convolutional neural network (CNN) used for filtering, information available from the encoder or decoder, in addition to the initial reconstructed image, can also be used as input to the convolutional neural network. In one embodiment, QP, block boundary information and prediction image can be used as additional channels of the input. The boundary information may help the CNN to understand where the blocking artifacts are, and thus, may improve the CNN since the network does not need to spending parameters looking for blocking artifacts. QP or prediction block also provide more information to the CNN. Such a convolutional neural network may replace all in-loop filters, or work together with other in-loop filters to more effectively remove compression artifacts.