Analysis of Affine Motion-Compensated Prediction in Video Coding

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Meuel, H.; Ostermann, J.: Analysis of Affine Motion-Compensated Prediction in Video Coding. In: IEEE Transactions on Image Processing 29 (2020), S. 7359-7374. DOI: https://doi.org/10.1109/TIP.2020.3001734

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Motion-compensated prediction is used in video coding standards like High Efficiency Video Coding (HEVC) as one key element of data compression. Commonly, a purely translational motion model is employed. In order to also cover non-translational motion types like rotation or scaling (zoom), e. g. contained in aerial video sequences such as captured from unmanned aerial vehicles (UAV), an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived. Using the rate-distortion theory and the displacement estimation error caused by inaccurate affine motion parameter estimation, the minimum required bit rate for encoding the prediction error is determined. In this model, the affine transformation parameters are assumed to be affected by statistically independent estimation errors, which all follow a zero-mean Gaussian distributed probability density function (pdf). The joint pdf of the estimation errors is derived and transformed into the pdfof the location-dependent displacement estimation error in the image. The latter is related to the minimum required bit rate for encoding the prediction error. Similar to the derivations of the fully affine motion model, a four-parameter simplified affine model is investigated. Both models are of particular interest since they are considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies. © 1992-2012 IEEE.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Elektrotechnik und Informatik

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1 image of flag of Germany Germany 16 76.19%
2 image of flag of United States United States 3 14.29%
3 image of flag of No geo information available No geo information available 1 4.76%
4 image of flag of Czech Republic Czech Republic 1 4.76%

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