A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels
We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macro-blocks of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks will be solved by estimating the proper covariance function of the Gaussian process defined over a region around the missing macro-blocks (i.e. its kernel function). In order to preserve block edges, the kernel is constructed adaptively by using the local edge related information. Moreover, we can achieve more improvement by local estimation of the kernel parameters. While restoring the prominent edges of the missing macro-blocks, the proposed method produces perceptually smooth concealed frames. Objective and subjective evaluations verify the effectiveness of the proposed method.
