Motion compensated image interpolation

Cafforio, C.; Rocca, F.; Tubaro, Stefano

doi:10.1109/26.47856

Cited by 79 publications

(32 citation statements)

References 10 publications

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“…However, this causes image quality deterioration. In recent years, motioncompensated FRUC (MC-FRUC) algorithm has been developed [1][2][3]. This algorithm generally has three steps: motion estimation (ME), motion vector smoothing (MVS), and motion compensated interpolation (MCI).…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Multidirectional motion estimation algorithm for frame rate up-conversion

2017

MATEC Web Conf.

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“…The former has been applied to frame-rate conversion for digital television. The image is divided into a set of non-overlapping blocks and forward and/or backward motion compensation is performed to create the interpolated frame (Cafforio et al, 1990). Occlusion effects are handled by analyzing the forward and backwards motion fields in order to decide from which frame to interpolate.…”

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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“…As the research progresses, motion compensated frame rate up-conversion (MC-FRUC) was proposed. MC-FRUC is composed of two parts, motion estimation (ME) and motion compensated interpolation (MCI) [1]. ME is the process that finds motion vector (MV).…”

Section: Introductionmentioning

confidence: 99%

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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Motion compensated image interpolation

Cited by 79 publications

References 10 publications

Multidirectional motion estimation algorithm for frame rate up-conversion

Multidirectional motion estimation algorithm for frame rate up-conversion

Frame Interpolation With Occlusion Detection Using a Time Coherent Segmentation

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

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