Abstract
We extend a stochastic model of hierarchical dependencies between wavelet coefficients of still images to the spatiotemporal decomposition of video sequences, obtained by a motion-compensated 2D+t wavelet decomposition. We propose new estimators for the parameters of this model which provide better statistical performances. Based on this model, we deduce an optimal predictor of missing samples in the spatiotemporal wavelet domain and use it in two applications: quality enhancement and error concealment of scalable video transmitted over packet networks. Simulation results show significant quality improvement achieved by this technique with different packetization strategies for a scalable video bit stream.
Highlights
Video coding schemes involving motion-compensated spatiotemporal (2D + t) wavelet decompositions [1, 2, 3] have been recently shown to provide very high coding efficiency and to enable complete spatiotemporal, SNR, and complexity scalability [4, 5, 6]
In the second part of this paper, we introduce a prediction method based on our stochastic model before presenting two applications of it: the quality improvement of scalable video and error concealment when packet losses occur during video transmission
We have first presented a statistical model for the spatiotemporal coefficients of a motion-compensated wavelet decomposition of a video sequence
Summary
Video coding schemes involving motion-compensated spatiotemporal (2D + t) wavelet decompositions [1, 2, 3] have been recently shown to provide very high coding efficiency and to enable complete spatiotemporal, SNR, and complexity scalability [4, 5, 6]. By extending the model proposed in [22, 23] to video sequences, we propose, in this paper, a stochastic modeling of the spatiotemporal dependencies in a motion-compensated 2D + t wavelet decomposition, in which we consider the conditional probability law of the coefficients in a given spatiotemporal subband to be Gaussian, with variance depending on the set of the spatiotemporal neighbors Based on this model, we provide new estimators for the proposed model, showing improved statistical performances. We provide new estimators for the proposed model, showing improved statistical performances We use it to build an optimal mean square predictor for missing coefficients, which is further exploited in two applications of transmitting over packet networks: a quality enhancement technique for resolutionscalable video bit streams and an error concealment method, both applied directly to the subbands of the spatiotemporal decomposition.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
