Adaptive frame rate up-conversion based on motion classification

Gao, Xinbo; Yang, Yue; Xiao, Bing

doi:10.1016/j.sigpro.2008.07.003

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…Another approach is to iteratively refine the motion vector field while propagating motion vector candidates [6]. Gao et al proposed the idea of Adaptive FRUC based on Motion Classification [7]. In this work, the scene is classified into global and local motion regions, and bidirectional or unidirectional ME is used based on the classification result.…”

Section: Previous Workmentioning

confidence: 99%

A Novel Approach to FRUC Using Discriminant Saliency and Frame Segmentation

Jacobson

Lee

Mahadevan

et al. 2010

IEEE Trans. on Image Process.

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Motion-compensated frame interpolation (MCFI) is a technique used extensively for increasing the temporal frequency of a video sequence. In order to obtain a high quality interpolation, the motion field between frames must be well-estimated. However, many current techniques for determining the motion are prone to errors in occlusion regions, as well as regions with repetitive structure. We propose an algorithm for improving both the objective and subjective quality of MCFI by refining the motion vector field. We first utilize a discriminant saliency classifier to determine which regions of the motion field are most important to a human observer. These regions are refined using a multistage motion vector refinement (MVR), which promotes motion vector candidates based upon their likelihood given a local neighborhood. For regions which fall below the saliency-threshold, a frame segmentation is used to locate regions of homogeneous color and texture via normalized cuts. Motion vectors are promoted such that each homogeneous region has a consistent motion. Experimental results demonstrate an improvement over previous frame rate up-conversion (FRUC) methods in both objective and subjective picture quality.

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Section: Previous Workmentioning

confidence: 99%

A Novel Approach to FRUC Using Discriminant Saliency and Frame Segmentation

Jacobson

Lee

Mahadevan

et al. 2010

IEEE Trans. on Image Process.

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“…Many motioncompensated interpolation (MCI) algorithms with motion estimation (ME) have been proposed for FRUC (Choi et al, 2000;Gao et al, 2008;Haan, 1999;Ha et al, 2004;Ojo and Haan, 1997). These methods use the motion information of two or more adjacent frames and motion vectors (MVs) are used to reconstruct a new frame from the corresponding frames using motion compensated interpolation (MCI).…”

Section: Introductionmentioning

confidence: 99%

Overlapped Block Motion Compensation for Frame Rate Up Conversion

Kim

Jeong

Lee³

2010

Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visu

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Recently, motion compensated interpolation methods are used to generate new frames in the frame rate up conversion. However, they often yield undesirable blocking artifacts due to inaccurate motion estimation. In order to mitigate these artifacts, we propose a new motion-compensated frame rate up conversion algorithm with overlapped block motion estimation. The new interpolated frame is obtained by overlapped block motion compensation of multiple motion estimation between the current and previous frames. Experimental results show the proposed method produces better perceptual quality and outperforms conventional motion-compensated methods in terms of PSNR. 2 PROPOSED METHOD 2.1 Motion Estimation and Motion Compensation Interpolation Methods To obtain motion vectors, we used uni-directional ME (UDME, Figure 1) and bi-directional ME (BDME, Figure 2) methods (Choi et al., 2000).

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Blur image identification with ensemble convolution neural networks

Wang

Zhang

2019

Signal Processing

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Adaptive frame rate up-conversion based on motion classification

Cited by 10 publications

References 21 publications

A Novel Approach to FRUC Using Discriminant Saliency and Frame Segmentation

A Novel Approach to FRUC Using Discriminant Saliency and Frame Segmentation

Overlapped Block Motion Compensation for Frame Rate Up Conversion

Blur image identification with ensemble convolution neural networks

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