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Competition Based Prediction for Skip Mode Motion Vector Using Macroblock Classification for the H.264 JM KTA Software

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4678))

Abstract

H.264/MPEG4-AVC achieves higher compression gain in comparison to its predecessors H.263 and MPEG4 part 2. This gain partly results from the improvement of motion compensation tools especially the variable block size, the 1/4-pel motion accuracy and the access to multiple reference frames. A particular mode among all Inter modes is the Skip mode. For this mode, no information is transmitted except the signaling of the mode itself. In our previous work we have proposed a competing framework for better motion vector prediction and coding, also including the Skip mode. This proposal has recently been adopted by the Video Coding Expert Group (VCEG) in the Key Technical Area-software (KTA) of H.264, which is the starting point for future ITU standardization activities. In this paper we propose an extension of this method based on the adaptation of two families of predictors for the Skip mode according to the video content and to statistical criteria. A systematic gain upon the previous method, with an average of 8.2% of bits saved compared to H.264 standard, is reported.

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References

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Jacques Blanc-Talon Wilfried Philips Dan Popescu Paul Scheunders

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© 2007 Springer-Verlag Berlin Heidelberg

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Laroche, G., Jung, J., Pesquet-Popescu, B. (2007). Competition Based Prediction for Skip Mode Motion Vector Using Macroblock Classification for the H.264 JM KTA Software. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_71

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  • DOI: https://doi.org/10.1007/978-3-540-74607-2_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74606-5

  • Online ISBN: 978-3-540-74607-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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