Motion compensation with higher order motion models for HEVC

Heithausen, Cordula; Vorwerk, Jan Hendrik

doi:10.1109/icassp.2015.7178208

Cited by 24 publications

(14 citation statements)

References 6 publications

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“…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%

“…For the quantization, the most common, used in References [3,8,9], is a quantization step of 1/512. Reference [10] evaluates different quantization step sizes, from 1/16 to 1/512.…”

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: Transform Computationmentioning

confidence: 99%

Section: Gradient-based Parameter Estimationmentioning

confidence: 99%

“…For the quantization, the most common, used in References [3,8,9], is a quantization step of 1/512. Reference [10] evaluates different quantization step sizes, from 1/16 to 1/512.…”

Section: Motion Parameter Prediction and Entropy Codingmentioning

confidence: 99%

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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“…Partitioning the frame in blocks of variable sizes [4] or using high-order motion models [5], [6] have been proposed as solutions to overcome the limitations of this model, at the price of an overhead to signal the partitions and increased complexity. Such solutions may also improve motion estimation in omnidirectional videos, as demonstrated in [7], where a rate-distortion optimal selection of translational, affine or quadratic motion model has been investigated for panoramic videos resulting from cylindrical or equirectangular map projection.…”

Section: Related Workmentioning

confidence: 99%

Deformable block-based motion estimation in omnidirectional image sequences

Simone

Frossard

Birkbeck

et al. 2017

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)

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Abstract-This paper presents an extension of block-based motion estimation for omnidirectional videos, based on a translational object motion model that accounts for the spherical geometry of the imaging system. We use this model to design a new algorithm to perform block matching in sequences of panoramic frames that are the result of the equirectangular projection. Experimental results demonstrate that significant gains can be achieved with respect to the classical exhaustive block matching algorithm in terms of accuracy of motion prediction. In particular, average quality improvements up to approximately 6 dB in terms of Peak Signal to Noise Ratio (PSNR), 0.043 in terms of Structural SIMilarity index (SSIM), and 2 dB in terms of spherical PSNR, can be achieved on the predicted frames.

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