Iterative True Motion Estimation for Motion-Compensated Frame Interpolation

Kim, DongYoon; Lim, Hyungjun; Park, HyunWook

doi:10.1109/tcsvt.2012.2207271

Cited by 31 publications

(8 citation statements)

References 16 publications

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“…Several FI methods have been proposed which try to detect occluded regions, and show improved performances compared with methods without occlusion handling. Kim et al [10] use linearity checking between the estimated forward and backward motion fields to detect occluded regions. Cho et al [11] use a bidirectional ME scheme that is based on feature trajectory tracking, which allows us to detect occluded regions.…”

Section: School Of Ee Andt University Of New South Wales Sydney Austmentioning

confidence: 99%

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Occlusion-aware temporal frame interpolation in a highly scalable video coding setting

Rüfenacht

Mathew

Taubman

2016

SIP

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Section: School Of Ee Andt University Of New South Wales Sydney Austmentioning

confidence: 99%

“…Kim et al . [10] use linearity checking between the estimated forward and backward motion fields to detect occluded regions. Cho et al .…”

Section: Introductionmentioning

confidence: 99%

Occlusion-aware temporal frame interpolation in a highly scalable video coding setting

Rüfenacht

Mathew

Taubman

2016

SIP

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“…In light of that, 3D Recursive Search (3DRS) was proposed based on the spatiotemporal correlation [10]. To track MVs as truly as possible, MVS imposes some smoothness constraints on BMA [11], so that more MV outliers can be effectively suppressed. MVS can also be explicitly implemented by median filtering and penalty terms [12], but this explicit approach increases the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion

Yue

2019

Future Internet

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In Multimedia Internet of Things (IoT), in order to reduce the bandwidth consumption of wireless channels, Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is often used to support the low-bitrate video communication. In this paper, we propose a spatial predictive algorithm which is used to improve the performance of MC-FRUC. The core of the proposed algorithm is a predictive model to split a frame into two kinds of blocks: basic blocks and absent blocks. Then an improved bilateral motion estimation is proposed to compute the Motion Vectors (MVs) of basic blocks. Finally, with the spatial correlation of Motion Vector Field (MVF), the MV of an absent block is predicted based on the MVs of its neighboring basic blocks. Experimental results show that the proposed spatial prediction algorithm can improve both the objective and the subjective quality of the interpolated frame, with a low computational complexity.

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“…Researchers are developing the algorithms for content aware ME to make the search more adaptive (Yi-Ching Liawa and Zuu-ChangHong, 2009). Characteristics of video data can be in terms of object acceleration, inertia, linearity (DongYoon Kim and Park, 2013) of the object etc. This paper is focusing on Homogeneity of the object in a macroblock with respect to adjacent macroblocks of the video frame.…”

Section: Introductionmentioning

confidence: 99%

Random Initial Search Points Prediction for Content Aware Motion Estimation in H.264

More¹,

Deshmukh²,

More³

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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Motion estimation algorithms used in video encoders are based on three important issues: selection of good initial search points, choice of appropriate search pattern and effective early termination criteria at different stages in algorithm.Motion vector prediction is also treated as initial search point prediction, in which possibility of good match block is predicted. Prediction is based on prior data from co-located and/or adjacent macroblocks from reference frame or current video frame respectively.Different search patterns contribute in achieving near accurate motion estimation. Different types of motion in real time videos can be tracked using different types of patterns. Early termination criteria at different stages in algorithm, avoid search at further possible locations which are pre-decided by pattern of search. This in turns reduces computations and motion estimation time.Proposed algorithm is combination of two concepts, content awareness and initial point prediction.Contents of video data is in terms of homogeneity coefficients. Initial search point prediction is used to avoid the search trapping into local minima.The algorithm is implemented on Reference Software of JM18.4 of H.264/AVC revised on 5th May 2011. The results of the implemented algorithm show that the total time taken for encoding and motion estimation time are less as compared with other algorithms for the videos of different resolutions.

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Iterative True Motion Estimation for Motion-Compensated Frame Interpolation

Cited by 31 publications

References 16 publications

Occlusion-aware temporal frame interpolation in a highly scalable video coding setting

Occlusion-aware temporal frame interpolation in a highly scalable video coding setting

A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion

Random Initial Search Points Prediction for Content Aware Motion Estimation in H.264

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