Improved higher order motion compensation in HEVC with block-to-block translational shift compensation

Heithausen, Cordula; Blaser, Max; Wien, Mathias; Ohm, Jens-Rainer

doi:10.1109/icip.2016.7532710

Cited by 13 publications

(22 citation statements)

References 6 publications

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“…However, this method allows the implementation to use standard block matching techniques that can reuse existing hardware or already implemented methods in software, while solving linear equations in the gradient-based approach requires a completely new implementation. They show a similar time and efficiency compared to Reference [9], but with fewer changes to the existing encoder.…”

Section: Introductionmentioning

confidence: 88%

“…In Reference [8,9], the authors suggest doing a 1/16 th sample interpolation using a eight-tap filter. While it is quite slow, as the gradient method converges quickly towards the optimal value, it does not add too much additional burden to the encoder.…”

Section: Transform Computationmentioning

confidence: 99%

“…In Reference [5,[7][8][9], a gradient method is used to estimate the affine motion parameters. This method is based on the Newton-Raphson method, which is a method that allows finding the root of a function with an iterative process.…”

Section: Gradient-based Parameter Estimationmentioning

confidence: 99%

“…Reference [9] improves the translational motion vector coding by estimating the change in the translation parameter between blocks. Block-to-block translational shift compensation (BBTSC) corrects the translational shift, allowing merge mode to be used much more often as there is no need to signal the motion vector difference.…”

Section: Motion Parameter Prediction and Entropy Codingmentioning

confidence: 99%

“…In the recent years, the most common implementation for obtaining the parameters is gradient-based. This gradient process is used in many methods, including the affine motion compensation in JEM [7], and methods based on HEVC [8,9]. Typically, the process starts with an initial estimation.…”

Section: Introductionmentioning

confidence: 99%

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Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation

et al. 2020

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This study proposes a lightweight solution to estimate affine parameters in affine motion compensation. Most of the current approaches start with an initial approximation based on the standard motion estimation, which only estimates the translation parameters. From there, iterative methods are used to find the best parameters, but they require a significant amount of time. The proposed method aims to speed up the process in two ways, first, skip evaluating affine prediction when it is likely to bring no encoding efficiency benefit, and second, by estimating better initial values for the iteration process. We use the optical flow between the reference picture and the current picture to estimate quickly the best encoding mode and get a better initial estimation. We achieve a reduction in encoding time over the reference of half when compared to the state of the art, with a loss in efficiency below 1%.

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Section: Introductionmentioning

confidence: 88%

Section: Transform Computationmentioning

confidence: 99%