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A learning-based video compression on low-quality data by unscented kalman filters with Gaussian process regression

With the ever increasing concern of vision-based video analysis and coding over resource-limited systems, this paper proposes a novel video coding scheme that exploits low-quality video data and formulates as an inverse learning based video reconstruction from online training by diverse stochastic processes. Given a sparsely sampled incomplete data, the intrinsic nonlocal and spatio-temporal geometric regularity related to online training examples in the key frames are considered as a state-dependent uncertainty estimation problem using Gaussian Process (GP) regression. Unlike non-parametric or exemplar-based sampling methods, we consider non-parametric system models for sequential state estimation by using the Unscented Kalman Filter (UKF) as the state estimator. It inherits the unscented transform for linearization to the transition function and the observation function. Once an approximate motion and observation model is available, it can naturally be incorporated to make a further performance improvement.