Motion vector processing for frame rate up conversion

Dane, G.; Nguyen, Truong Q.

doi:10.1109/icassp.2004.1326543

Cited by 52 publications

(17 citation statements)

References 10 publications

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“…Occlusion effects are handled by analyzing the forward and backwards motion fields in order to decide from which frame to interpolate. In (Dane and Nguyen, 2004) the authors conclude that the averaging technique is appropriate if forward and backward prediction errors are equal. In other cases, they conclude with theoretical results that the filter taps associated to the motion compensation have to be adapted to the reliability of the motion vectors.…”

Section: Previous Workmentioning

confidence: 99%

Frame Interpolation With Occlusion Detection Using a Time Coherent Segmentation

Sadek¹,

Ballester²,

Garrido³

et al. 2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:In this paper we propose an interpolation method to produce a sequence of plausible intermediate frames between two input images. The main feature of the proposed method is the handling of occlusions using a time coherent video segmentation into spatio-temporal regions. Occlusions and disocclusions are defined as points in a frame where a region ends or starts, respectively. Out of these points, forward and backward motion fields are used to interpolate the intermediate frames. After motion-based interpolation, there may still be some holes which are filled using a hole filling algorithm. We illustrate the proposed method with some experiments.

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Section: Previous Workmentioning

confidence: 99%

Frame Interpolation With Occlusion Detection Using a Time Coherent Segmentation

Sadek¹,

Ballester²,

Garrido³

et al. 2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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show abstract

“…are computed according to (6), and R i+1 t (k, l) is computed according to (8). To make a decision whether the FRUC will be terminated in the current iteration, we compute the distortion of the interpolated pixels within the training windowsR t−1 andR t+1 between two successive iterations as well as the distortion between the actual and interpolated pixels within the training windowR t .…”

Section: Self-feedback Weight Trainingmentioning

confidence: 99%

“…To resolve such a problem, many motion vector postprocessing methods are proposed to smooth the motion field and improve the subjective perception [3], [7], [8]. What is more, for the areas with small objects, irregular shaped objects, and object boundaries, fixed size block MC usually does not work well.…”

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confidence: 99%

A Spatio-Temporal Auto Regressive Model for Frame Rate Upconversion

Zhang

Zhao

et al. 2009

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-This paper proposes a spatio-temporal auto regressive (STAR) model for frame rate upconversion. In the STAR model, each pixel in the interpolated frame is approximated as the weighted combination of a sample space including the pixels within its two temporal neighborhoods from the previous and following original frames as well as the available interpolated pixels within its spatial neighborhood in the current tobe-interpolated frame. To derive accurate STAR weights, an iterative self-feedback weight training algorithm is proposed. In each iteration, first the pixels of each training window in the interpolated frames are approximated by the sample space from the previous and following original frames and the to-beinterpolated frame. And then the actual pixels of each training window in the original frame are approximated by the sample space from the previous and following interpolated frames and the current original frame with the same weights. The weights of each training window are calculated by jointly minimizing the distortion between the interpolated frames in the current and previous iterations as well as the distortion between the original frame and its interpolated one. Extensive simulation results demonstrate that the proposed STAR model is able to yield the interpolated frames with high performance in terms of both subjective and objective qualities.Index Terms-Auto regressive model, frame rate upconversion, self-feedback, training window.

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“…Alparone et al proposed using Adaptively Weighted Vector Median Filter (AWVMF) based on prediction residues to obtain a smoother motion field [2]. Dane et al presented a method applying vector median and low pass filtering after MV classification based on the angle variance among its neighboring MVs [4]. In [6] This letter presents an edge-based approach for an MV smoothing method.…”

Section: Introductionmentioning

confidence: 99%

Edge-Based Motion Vector Processing for Frame Interpolation Based on Weighted Vector Median Filter

Park

Kim

Hoon

2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a new motion vector smoothing algorithm using weighted vector median filtering based on edge direction for frame interpolation. The proposed WVM (Weighted Vector Median) system adjusts the weighting values based on edge direction, which is derived from spatial coherence between the edge direction continuity of a moving object and motion vector (MV) reliability. The edge based weighting scheme removes the effect of outliers and irregular MVs from the MV smoothing process. Simulation results show that the proposed algorithm can correct wrong motion vectors and thus improve both the subjective and objective visual quality compared with conventional methods.

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Motion vector processing for frame rate up conversion

Cited by 52 publications

References 10 publications

Frame Interpolation With Occlusion Detection Using a Time Coherent Segmentation

Frame Interpolation With Occlusion Detection Using a Time Coherent Segmentation

A Spatio-Temporal Auto Regressive Model for Frame Rate Upconversion

Edge-Based Motion Vector Processing for Frame Interpolation Based on Weighted Vector Median Filter

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