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This letter presents a novel Motion Vector (MV) recovery method which is based on Mean Shift (MS) procedure. According to motion continuity, MVs in local area should be similar. If projecting MV into 2-D feature space, local MVs in the feature space tend to cluster closely. To estimate the lost MVs in local area, recovery of lost MVs is modeled as clustering operation. MS procedure is applied to enforce each lost MV in the feature space to shift to the position where dominant MVs are gathered. Meanwhile, bandwidth estimation is statistically characterized by the variation of local standard de-viations; weighted value calculation is determined by estimation of overall standard deviation. Simu-lation results demonstrate their better performance when compared with other MV recovery ap-proaches and low computation cost.
This letter presents a novel Motion Vector (MV) recovery method which is based on Mean Shift (MS) procedure. According to motion continuity, MVs in local area should be similar. If projecting MV into 2-D feature space, local MVs in the feature space tend to cluster closely. To estimate the lost MVs in local area, recovery of lost MVs is modeled as clustering operation. MS procedure is applied to enforce each lost MV in the feature space to shift to position where dominant MVs are gathered. Simu-lation results demonstrate their better performance when compared with other MV recovery ap-proaches and low computation costs .