A Stochastic Model for Video and its Information Rates

We propose a stochastic model for video and compute its information rates. The model has two sources of information representing ensembles of camera motion and visual scene data (i.e. "realities"). The sources of information are combined generating a vector process that we study in detail. Both lossless and lossy information rates are derived. The model is further extended to account for realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Experiments with synthetic sources suggest that in the presence of scene motion, simple hybrid coding using motion estimation with DPCM can be suboptimal relative to the true rate-distortion bound.

Presented at:
Data Compression Conference 2007, Snowbird, Utah, USA, March 27-29, 2007

 Record created 2010-06-21, last modified 2018-01-28

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