This work proposes a novel Deep Learning technique to increase the efficiency of currently available video compression techniques based on motion compensation. The goal is to improve the frame prediction task, whereby a more accurate prediction of the motion from the reference frames to the target frame allows to reduce the rate needed to encode the residual. This is achieved by means of a convolutional neural network (CNN) architecture that processes the basic block-based motion-compensated prediction of the current frame as well as predictions from past reference frames. This method allows to reduce typical artifacts such as blockiness, and achieves a more accurate prediction of motion thanks to the representation capabilities of CNNs, leading to smaller prediction residuals. Preliminary results show that the proposed approach is capable of providing BD-rate gains up to 6%.


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    Title :

    Deep Multiframe Enhancement for Motion Prediction in Video Compression



    Publication date :

    2021-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Multiframe Structure from Motion in Perspective

    Oliensis, J. / IEEE / Computer Society et al. | British Library Conference Proceedings | 1995


    Dealing with Noise in Multiframe Structure from Motion

    Inigo Thomas, J. / Oliensis, J. | British Library Online Contents | 1999


    Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA

    Vidal, R. / Tron, R. / Hartley, R. | British Library Online Contents | 2008


    Multiframe detector/tracker: optimal performance

    Bruno, M.G.S. / Moura, J.M.F. | IEEE | 2001