Design and implementation of median filter based adaptive motion vector smoothing for motion compensated frame rate up-conversion

Kang, Suk‐Ju; Yoo, Dong-Gon; Lee, Sung-Kyu; Kim, Young Hwan

doi:10.1109/isce.2009.5156958

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…In this section, the performance of the proposed CA-MVS algorithm is evaluated by testing it on different video sequences and comparing the results with those obtained by the traditional MVS algorithms including MF [14], VMF [15], WVMF [16], S-MVS [19], and T-MVS [20]. An MCFI algorithm is also combined by BME, CA-MVS, and OBMC and compared with the recent state-of-the-art MCFI algorithms [1,3,7,8] from objective perspectives.…”

Section: Resultsmentioning

confidence: 99%

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Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

Yin

Sun

et al. 2021

Wireless Communications and Mobile Computing

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Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms.

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Section: Resultsmentioning

confidence: 99%

“…A typical MVS method is Median Filter (MF) [14], which replaces the MV of a block with the mean of MVs of its adjacent blocks. When an MV outlier occurs in a flat region, MF is not to adjust this outlier along with the major direction of neighboring MVs but to produce a new MV biased toward the major direction.…”

Section: Introductionmentioning

confidence: 99%

Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

Yin

Sun

et al. 2021

Wireless Communications and Mobile Computing

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“…Therefore, MV filtering is necessary for FRUC. A MV smoothing method based on the median filter [4] was designed to make MVs more consistent and the motion field smoother.…”

Section: Introductionmentioning

confidence: 99%

Motion vector refinement for frame rate up conversion on 3D video

Liu

Fan

Gao

et al. 2013

2013 Visual Communications and Image Processing (VCIP)

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With the rapid development of digital video technology, frame rate up conversion is widely used. In this paper, a novel motion vector refinement method for frame rate up conversion on depth based 3D video is proposed. Our method involves two major stages in frame rate up conversion which are motion estimation and motion vector filtering. In the motion estimation process, the depth constraint to block matching algorithm is introduced in the bi-directional motion estimation method to obtain the motion vectors. In the motion vector filtering process, a depth-guided filter is designed to enhance the consistence of motions in the same depth plane. The refined motion vectors are used for frame interpolation. Experimental results show that the proposed method achieves 0.45 dB gain in terms of PSNR on average and improves the visual quality of the frame rate up-converted video.

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Frame Rate Up-Conversion under Stationary Constraints of Spatio-Temporal Local Motion

Ma¹,

Li²,

Yu³

2022

2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

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Design and implementation of median filter based adaptive motion vector smoothing for motion compensated frame rate up-conversion

Cited by 11 publications

References 4 publications

Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

Motion‐Compensated Frame Interpolation Using Cellular Automata‐Based Motion Vector Smoothing

Motion vector refinement for frame rate up conversion on 3D video

Frame Rate Up-Conversion under Stationary Constraints of Spatio-Temporal Local Motion

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