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A Dense Motion Field of a digital image is a vector field that associates a two dimensional motion vector to every pixel in the image. The motion vector is often the projection of the three-dimensional motion of objects in a perceived scene onto the image plane. Various Optical Flow methods can be used to estimate the dense motion field from a video sequence of two or more frames. Motion estimation is an important part of most video coding schemes because it enables us to exploit the high degree of temporal redundancy present. Though block matching algorithms (BMA) yield coarse and piecewiseconstant fields, they are very popular due to their simplicity and low bit overhead. In this paper, we propose to use a more advanced gradient-based technique to overcome the disadvantages of BMA. A dense motion field is estimated and compressed using a hierarchical finite element (HFE) representation, leading to an efficient, highly parallel, iterative, multiresolution optimization algorithm. The scheme also uses multiresolution measurements and a coarse-to-fine strategy to estimate large displacements. At comparable bit rates, the motion fields are much smoother and more natural than those produced by BMA. Coding gains of about 0.6 dB were obtained on Claire. More importantly, substantial visual improvements were obtained, mainly due to improved performance near the edges. Bold text
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