Motion vector smoothing for motion-compensated frame rate up-conversion

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

doi:10.1109/socdc.2010.5682898

Cited by 3 publications

(7 citation statements)

References 5 publications

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“…As we used 3-level Gaussian pyramid, proposed algorithm is about 80 times faster than [7] with 0.1dB PSNR gain in average. Also, proposed algorithm is about 2 times faster than [8] with 0.17dB PSNR gain in average. Fig.…”

Section: Resultsmentioning

confidence: 93%

“…When evaluating quality of algorithm, we calculated peak signal to ratio (PSNR) with original even frames and interpolated even frames. Table 1 and Table 2 shows PSNR and time results comparing with conventional algorithms [7], [8] and proposed algorithm. As we used 3-level Gaussian pyramid, proposed algorithm is about 80 times faster than [7] with 0.1dB PSNR gain in average.…”

Section: Resultsmentioning

confidence: 99%

“…Among them, pyramid structure is most popular because it is easy to implement both for software and hardware [9]. However, conventional algorithms used simple subsampling method which reduces accuracy of ME [7] - [8]. To overcome this weakness, our proposed algorithm uses 3-level Gaussian pyramid structure using 3×3 Gaussian mask.…”

Section: Gaussian Pyramidmentioning

confidence: 99%

“…In the conventional algorithms [7] - [8], they considered only one MV that is located on the same position of current block in the reference frame. However, considering only one MV candidate caused MV shifting error despite of their additional process to refine it.…”

Section: Motion Vector Shiftingmentioning

confidence: 99%

“…Consequently, miss match between unilateral MV and bilateral MV caused loss of quality despite of their refinement process. To overcome their weakness, Yoo et al proposed direction-select ME (DSME) which use both forward and backward unilateral MV as an initial MV to estimate accurate MV [8]. However, they also used simple subsampling method for reducing computational complexity which is not proper to find accurate MV.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Motion-Compensated Frame Rate Up-Conversion Using Gaussian Pyramid and Guidance Motion Vector

Park

2017

MATEC Web Conf.

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Abstract. This paper proposes fast and accurate frame rate up-conversion algorithm for both software and hardware. The proposed algorithm first construct Gaussian pyramid structure using 3×3 Gaussian mask. After constructing Gaussian pyramid, forward and backward unilateral motion estimation is implemented at the top level of the pyramid. Then, motion vector field that is obtained at the top level is projected onto the middle level and the bottom level of the pyramid with unilateral motion vector refinement. Next, on the bottom level, we implement motion vector smoothing and proposed motion vector shifting with bilateral motion vector refinement. By shifting unilateral motion vector to bilateral motion vector, we could avoid generating holes and overlapped regions. Finally, we select final MV between forward and backward MVs according to their reliability. After all the processes, we interpolate new frames by using overlapped block motion compensation. Experimental results show that proposed algorithm is up to 80 times faster than conventional algorithm with 0.1dB peak signal-to-noise gain in average.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Gaussian Pyramidmentioning

confidence: 99%

Section: Motion Vector Shiftingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical Motion-Compensated Frame Rate Up-Conversion Using Gaussian Pyramid and Guidance Motion Vector

Park

2017

MATEC Web Conf.

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Multidirectional motion estimation algorithm for frame rate up-conversion

2017

MATEC Web Conf.

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Abstract. In this paper, we propose a new motion estimation algorithm for motion-compensated frame rate up-conversion. To reduce computational complexity, it constructs a hierarchical structure using Gaussian image pyramid and unidirectionally estimates motion vectors (MVs) between two original frames at the top level. Since the accuracy of the motion vector may deteriorate at low resolution, each block has five candidates for motion vector so that disadvantages of hierarchical motion estimation are reduced. After motion estimation (ME), MVs of bottom level are corrected based on the sum of absolute difference (SAD) values of the blocks. All unidirectional MVs are assigned to bidirectional MVs, and new frames are interpolated using these MVs. As a result, proposed algorithm shows up to 1.3 dB increase in PSNR compared with conventional algorithm with low computational complexity, and it shows clearer images than conventional algorithms.

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