Abstract
We present a new search region prediction method using frequency sensitive competitive learning vector quantization for motion estimation of image sequences. The proposed method can decrease the computation time because of the smaller number of search points compared to other methods, and also reduces the bits required to represent motion vectors. The results of experiments show that the proposed method provides competitive PSNR values compared to other block matching algorithms while reducing the number of search points and minimizing the complexity of the search region prediction process.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ryu, D., Kim, H. (2006). Search Region Prediction for Motion Estimation Based on Neural Network Vector Quantization. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_67
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DOI: https://doi.org/10.1007/11760023_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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