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A pixel of a frame is considered as an intensive one if the luminance pixel difference between the adjacent frames is bigger than 100.
Therefore, hidden Markov models (HMM) have been introduced into the BWE schemes to further capture the temporal relationship between the adjacent frames of audio signals [16]-[18] [16]-[18]
Usually, SI is generated at the decoder by the motion-compensated interpolation (MCI) from the past and future key frames under the assumption that the motion trajectory between the adjacent frames is translational with constant velocity.
As to the quantized norms in G.719, a large percentage, 98.9%, of all the frames have the correlation (i.e., the correlation coefficient is higher than 0.5) between the adjacent frames which enables larger compression.
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Moreover, because the difference between the adjacent frame configurations is small, the negative effect of frame reconfiguration is neglectable, which is helpful to the stability of the network.
In one diptych, a blood-spattered tree leans towards another in the adjacent frame.
The changes in the vanishing point position between the two adjacent frames are within a small range.
The 1D Gaussian model is chosen to analyze the data changes of vanishing points between the two adjacent frames.
Thus, we can approximate the amount of motion using the differences between adjacent frames (e.g., by analyzing the FD-sequence), rather than direct computation of the motion vectors.
The translation between adjacent frames is estimated using the left and right end points of the extracted skeletal segments as landmarks.
Considering the weights at each level, the distance between adjacent frames can be computed: (20).
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