High frame rate Motion Compensated Frame Interpolation in High-Definition video processing

Lee, Yen-Lin; Nguyen, Truong Q.

doi:10.1109/icassp.2010.5495214

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Typically, motion vectors (MVs) used in interpolation are estimated using a block matching algorithm (BMA) [2]- [14]. Block-based FRUC methods can improve quality of the interpolated frames, because of the attempt to model the objects' motion between the previous and next frames.…”

Section: Bi-directional Mesh-based Frame Rate Upconversion With a Denmentioning

confidence: 99%

Bidirectional Mesh-Based Frame Rate Up-Conversion

Min

Sim

et al. 2015

IEEE MultiMedia

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In this paper, we propose a new frame rate upconversion (FRUC) method for temporal video quality enhancement. The proposed algorithm generates an interpolated frame in between two given frames based on a bi-directional mesh interpolation (BMI), in order to cope with not only translation, but also with scale and rotation changes. BMI performance is highly influenced by the accuracy of the correspondences between the control points in the two frames. To achieve an accurate dense motion vector map (MVM) through bi-directional and unidirectional motion estimation, an initial MVM is formed by the transmitted motion vectors from coded bitstream with low computational complexity. Then, the interpolated frame is generated by frame-based BMI with the dense MVM. In our experiments, we found out that the proposed algorithm is about 2dB better than several conventional FRUC methods. Furthermore, block artifacts and blur artifacts are significantly diminished by the proposed algorithm. Index Terms-Frame rate up-conversion, mesh interpolation, uni-directional motion estimation, bi-directional motion estimation I. INTRODUCTIONrame rate up-conversion (FRUC) refers to the postprocessing methods whose goal is to improve video quality by increasing the frame rate. A number of FRUC algorithms have been developed [1]- [15], and they can be classified into two main categories. One category contains methods that generate interpolated frames without explicitly considering motion. These algorithms analyze mathematical relations regarding pixel intensity changes among neighboring frames. The second category includes motion-based techniques. Most of these methods generate interpolated frames with the help of block-based motion estimation and compensation [2]- [15]. These approaches are able to improve video quality with natural continuity of motion. However, the block-based translational motion model can lead to blocking artifacts.Mesh-based techniques [17], [18] are another alternative in an attempt to represent more realistic motion. Like most other interpolation methods, the mesh-based interpolation is sensitive to occlusions. In order to deal with these occlusions, the proposed method interpolates the target frame through bi-

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Section: Bi-directional Mesh-based Frame Rate Upconversion With a Denmentioning

confidence: 99%

Bidirectional Mesh-Based Frame Rate Up-Conversion

Min

Sim

et al. 2015

IEEE MultiMedia

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show abstract

“…To solve these problems, frame rate up conversion (FRUC) technology arises spontaneously. FRUC can achieve frame rate conversion between different video scanning formats, so as to reduce the holding time of LCD and eliminate the above problems effectively [1]. For example, the frame rate can be increased from 50/60fps to 100/120fps to make the display more fluent.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Parallel FPGA Architecture for Bi-Directional Motion Estimation

Huang¹,

Deng²,

Dong-lian³

et al. 2015

IJHIT

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Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

Lee

2021

Computer Graphics Forum

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In this paper, we propose a two-step temporal interpolation network using forward advection to generate smoke simulation efficiently. By converting a low frame rate smoke simulation computed with a large time step into a high frame rate smoke simulation through inference of temporal interpolation networks, the proposed method can efficiently generate smoke simulation with a high frame rate and low computational costs. The first step of the proposed method is optical flow-based temporal interpolation using deep neural networks (DNNs) for two given smoke animation frames. In the next step, we compute temporary smoke frames with forward advection, a physical computation with a low computational cost. We then interpolate between the results of the forward advection and those of the first step to generate more accurate and enhanced interpolated results. We performed quantitative analyses of the results generated by the proposed method and previous temporal interpolation methods. Furthermore, we experimentally compared the performance of the proposed method with previous methods using DNNs for smoke simulation. We found that the results generated by the proposed method are more accurate and closer to the ground truth smoke simulation than those generated by the previous temporal interpolation methods. We also confirmed that the proposed method generates smoke simulation results more efficiently with lower computational costs than previous smoke simulation methods using DNNs.

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High frame rate Motion Compensated Frame Interpolation in High-Definition video processing

Cited by 5 publications

References 7 publications

Bidirectional Mesh-Based Frame Rate Up-Conversion

Bidirectional Mesh-Based Frame Rate Up-Conversion

An Advanced Parallel FPGA Architecture for Bi-Directional Motion Estimation

Two‐step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

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