Abstract
In this paper we present a new method for video compression. Our approach is based on a well known neural network image compression algorithm: predictive vector quantization (PVQ). In this method of image compression two different neural network structures are exploited in the following elements of the proposed system: a competitive neural networks quantizer and a neuronal predictor. It is important for the image compression based on this approach to correctly detect key frame in order to improve performance of the algorithm. For key frame detection our method uses a SKFD method based on the SURF algorithm.
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Acknowledgments
The work presented in this paper was supported by a grant BS/MN-1-109-301/14/P “Clustering algorithms for data stream—in reference to the Content-Based Image Retrieval methods (CBIR)”. The work presented in this paper was supported by a grant BS/MN 1-109-302/14/P “New video compression method using neural image compression algorithm”.
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Grycuk, R., Knop, M. (2016). Neural Video Compression Based on SURF Scene Change Detection Algorithm. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_13
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DOI: https://doi.org/10.1007/978-3-319-23814-2_13
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