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This FRU algorithm performs forward motion estimation to estimate an initial interpolated frame from the (t) frame to the (t – 1) frame.
Since we do not need accurate motion estimation to classify the shots or for adaptive indexing, an estimate of the amount of motion in the shot is enough.
Then we use patch-based bidirectional motion estimation to generate a smooth Motion Vector Felid MVFF).
We exploit the motion estimation to derive an automatic stopping criterion.
The proposed method employs hierarchical motion estimation to enhance stability of motion estimation.
The proposed algorithm can easily be combined with most of the methods of fast motion estimation to reduce computational cost.
Similar(44)
A large (64 by 64 pixels) block size is used in order to reduce the motion estimation sensitivity to movement of small objects within the frame.
Based on our prior work [9], a novel progressive DVC architecture is proposed to mitigate the limitations of such blind motion estimation and to further explore the intrinsic spatial redundancy of each frame.
Our proposed technique extends the quarter-pixel motion estimation scheme to satisfy the data hiding capacity needs and to ensure the low PSNR Y and bit-rate distortions.
We extend the proposed motion estimation algorithm to work with blocks of variable sizes, in order to better capture local motion characteristics, thus improving in terms of rate-distortion behavior.
To this end, the encoder features a novel low-complexity motion estimation technique to approximate the side-information (SI) available at the decoder.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com