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direction estimation, pixel filling.
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After the global motion estimation, some pixels, especially object pixels, may experience different motions, which are not faithfully represented by the global motion model.
Our method can be divided into two substeps: (i) direction estimation, (ii) pixel filling. .
The estimation of pixel motion in two consecutive frames yields the optical flow computation [28].
Conventional FRU algorithms have been developed by utilizing temporal redundancy, as they perform motion estimation with pixel intensity within a fixed search range.
In the present article, we address an efficient solution for dense and robust motion estimation per pixel related with GPU memory consumption, which limits the GPU viability.
Further, assuming that all the parameters are unknown, a simple approach is proposed to estimate the expectation (or location) parameter of each coefficient by using linear properties of DCT as well as estimation of pixel expectation in the spatial domain; the variance (or scale) parameter is also estimated locally. 4.
We also extend our algorithm for pixel-level transmission distortion estimation to pixel-level end-to-end distortion estimation.
The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and estimation of new pixels for replacing the corrupted pixels.
In the stage of synchronized frames enhancement, disparity estimation is pixel-based; hence, the computational complexity of this stage is proportional to the size of resolution basically.
Considering the present of targets in the sample set disturbing the estimation, the pixels which may comprise target signal should be removed.
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