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We propose the fast optical flow extractor, a filtering method that recovers artifact-free optical flow fields from HEVC-compressed video. To extract accurate optical flow fields, we form a regularized optimization problem that considers the smoothness of the solution and the pixelwise confidence weights of an artifact- ridden HEVC motion field. Solving such an optimization problem is slow, so we first convert the problem into a confidence-weighted filtering task. By leveraging the already-available HEVC motion parameters, we achieve a 100-fold speed-up in the running times compared to similar methods, while producing subpixel-accurate flow estimates. The fast optical flow extractor is useful when video frames are already available in coded formats. Our method is not specific to a coder, and works with motion fields from video coders such as H.264/AVC and HEVC. [ABSTRACT FROM AUTHOR]

This paper proposes graph Laplacian regularization for robust estimation of optical flow. First, we analyze the spectral properties of dense graph Laplacians and show that dense graphs achieve a better trade-off between preserving flow discontinuities and filtering noise, compared with the usual Laplacian. Using this analysis, we then propose a robust optical flow estimation method based on Gaussian graph Laplacians. We revisit the framework of iteratively reweighted least-squares from the perspective of graph edge reweighting, and employ the Welsch loss function to preserve flow discontinuities and handle occlusions. Our experiments using the Middlebury and MPI-Sintel optical flow datasets demonstrate the robustness and the efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

We address the problem of decoding joint photographic experts group (JPEG)-encoded images with less visual artifacts. We view the decoding task as an ill-posed inverse problem and find a regularized solution using a convex, graph Laplacian-regularized model. Since the resulting problem is non-smooth and entails non-local regularization, we use fast high-dimensional Gaussian filtering techniques with the proximal gradient descent method to solve our convex problem efficiently. Our patch-based ”coefficient graph” is better suited than the traditional pixel-based ones for regularizing smooth non-stationary signals such as natural images and relates directly to classic non-local means de-noising of images. We also extend our graph along the temporal dimension to handle the decoding of M-JPEG-encoded video. Despite the minimalistic nature of our convex problem, it produces decoded images with similar quality to other more complex, state-of-the-art methods while being up to five times faster. We also expound on the relationship between our method and the classic ANCE method, reinterpreting ANCE from a graph-based regularization perspective. [ABSTRACT FROM AUTHOR]