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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors combine two recent advances in Gaussian processes, spectral mixture kernels [5], and scalable Gaussian processes for data in grids [14, 22 see below], in order to tackle applications with high amount of data points, like texture extrapolation, inpainting, and video extrapolation. The paper includes a thorough evaluation of the framework proposed, and comparisons against sparse GP methods, with general purpose covariance functions, and spectral mixture kernels. Quality The paper is technically sound. The framework proposed by the authors achieves outstanding results in the different applications studied in the paper.