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A kernel-based statistical analysis of the residual error in video coding

 
: De-Luxan-Hernandez, S.; Marpe, D.; Müller, K.-R.; Wiegand, T.

:

Liatsis, P. ; Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, United Kingdom and Republic of Ireland Section; European Association for Signal Processing -EURASIP-:
22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015. Proceedings : London, United Kingdom, 10-12 September 2015
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-8352-3 (Print)
ISBN: 978-1-4673-8354-7
ISBN: 978-1-4673-8353-0
S.192-195
International Conference on Systems, Signals and Image Processing (IWSSIP) <22, 2015, London>
Englisch
Konferenzbeitrag
Fraunhofer HHI ()

Abstract
Video compression techniques exploit the statistical redundancy present in video signals to efficiently reduce the amount of information sent to the decoder. We contribute with a kernel-based analysis of the residual error blocks. In particular, we borrow dimension reduction techniques from machine learning, namely Principal Component Analysis (PCA) and nonlinear Kernel Principal Component Analysis (KPCA), to assess the spatial structure of block residuals. Interestingly, a nonlinear structure is observed that correlates to the rate-distortion costs of the blocks. Simulations by using a test set of videos with cropped Ultra High Definition (UHD) resolution show interesting results.

: http://publica.fraunhofer.de/dokumente/N-383055.html