Journal Article10.1016/J.IMAGE.2017.02.010
Motion-compensated frame interpolation using patch-based sparseland model
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TL;DR: Using patch-based sparseland model, a novel Motion-Compensated Frame Interpolation (MCFI) method is designed that outperforms the existing algorithms in both objective and subjective picture qualities, but it introduces a high computational complexity in the meantime.
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Abstract: Using patch-based sparseland model, we design a novel Motion-Compensated Frame Interpolation (MCFI) method. Optical-flow estimation is first adopted to generate a reliable Motion Vector Field (MVF) from the previous frame to the following frame. Then we use patch-based bidirectional motion estimation to generate a smooth Motion Vector Felid (MVF). In the last step, we extract patches from reference frames along the motion trajectories, and perform Principle Component Analysis (PCA) to generate dictionaries that code the patches with various local structures. The sparseland model becomes the prior knowledge of the intermediate frame to fuse into the Motion Compensated Interpolation (MCI) by maximum a posteriori (MAP) criterion. By iterative numerical computing, we solve this sparseland-prior reconstruction model, and acquire a sparsity-preferred interpolated frame. Experimental results show that our method outperforms the existing algorithms in both objective and subjective picture qualities, but it also introduces a high computational complexity in the meantime. We design a patch-based Bidirectional Motion Estimation (BME) to assign a unique Motion Vector (MV) for each patch by using optical-flow ME.We construct the sparse model of the intermediate frame according to the MV of each pixel output by the BME module. This sparseland priori is formulated as a Maximum a Posteriori (MAP) estimation problem under the Bayesian framework, and this MAP estimation problem becomes the non-linear sparseland-prior reconstruction model.
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Citations
A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement
TL;DR: A fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity and the echocardiographic-specific method is proposed.
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Hierarchical prediction-based motion vector refinement for video frame-rate up-conversion
TL;DR: Experimental results show that the proposed MC-FRUC approach has low computational complexity and outperforms state-of-the-art works in both objective and subjective qualities of interpolated frames.
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Frame Rate Up-Conversion Using Bi-Directional Optical Flows With Dual Regularization
Rahul Vanam,Yuriy Reznik +1 more
- 01 Oct 2020
TL;DR: A frame rate up-conversion (FRUC) scheme that uses optical flows in forward and backward directions and with two different regularization parameters to derive alternative motion vector fields that yield significantly higher quality compared to existing FRUC algorithms.
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A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
Yanli Li,Wendan Ma,Yue Han +2 more
TL;DR: 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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Low complexity Phase-based Interpolation for side information generation for Wyner-Ziv coding at DVC decoder
Shahzad Khursheed,Varun Jeoti,Nasreen Badruddin,Manzoor Ahmed Hashmani +3 more
- 20 Jul 2020
TL;DR: Phase-Based Interpolation (PBI) is introduced in this work for side information generation and simulated results show that the PBI computational complexity remains low for all kinds of videos.
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