• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.309-312
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• 2019

본 논문에서는 계산의 복잡성을 줄이고 피크 신호 대 잡음 비율(PSNR) 성능을 개선하기 위한 새로운 프레임 상향 변환 (Frame Rate Up Conversion) 알고리즘을 제안한다. 제안된 알고리즘을 사용하기 위한 모션 추정 과정(Motion Estimation) 은 이전 프레임과 현재 프레임에서 마크로블록(Macroblock) 값의 최소 차이값(Sum of absolute differences) 을 이용하여 보간된 프레임(Interpolated Frame) 의 마크로블록이 가지게 되는 모션 벡터 값을 추출한다. 이 때 반복된 배경 패턴 및 여러 움직임들 때문에 모션 추정 과정에서 출력되는 벡터값이 비정상적으로 출력되는 경우가 있다. 여기서 제안된 알고리즘을 통해 모션 벡터값들의 특이치(Outlier) 를 검출하고 이를 교정하기 위한 분산값(Variance) 을 이용하여 모션 벡터 평활화 작업(Motion Vector Smoothing) 을 거친다. 이와 같이 제안된 알고리즘을 이용하여 실험한 결과값으로 프레임률 상향 변환 과정을 통해 상대적으로 계산의 복잡성은 낮으면서 양호한 PSNR 값이 출력됨을 확인할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.1-12
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• 2019

This paper proposes a new motion vector smoothing and motion compensation method to increase the frame rate of videos. The proposed method reduces false motion vector smoothing by improving the weight with reflecting accuracy to overcome the limitation of the conventional motion vector smoothing using the adaptively weighted vector median. Also, to improve the interpolated image quality of the conventional OBMC(Overlapped Block Motion Compensation), a scheme that divides an original block into 4 sub-blocks and then generates the interpolated frame using the reestimated motion vector for each sub-block is proposed. The simulation results prove that the proposed method can provide much better objective and subjective image quality than the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2448-2463
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• 2013

The traditional block-based motion compensation methods in frame rate up-conversion (FRUC) only use a single uniquely motion vector field. However, there will always be some mistakes in the motion vector field whether the advanced motion estimation (ME) and motion vector analysis (MA) algorithms are performed or not. Once the motion vector field has many mistakes, the quality of the interpolated frame is severely affected. In order to solve the problem, this paper proposes a novel joint overlapped block motion compensation method (8J-OBMC) which adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly interpolate the target block. Since the smoothness of motion filed makes the motion vectors of 8-neighbor blocks around the interpolated block quite close to the true motion vector of the interpolated block, the proposed compensation algorithm has the better fault-tolerant capability than traditional ones. Besides, the annoying blocking artifacts can also be effectively suppressed by using overlapped blocks. Experimental results show that the proposed method is not only robust to motion vectors estimated wrongly, but also can to reduce blocking artifacts in comparison with existing popular compensation methods.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.138-145
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• 2018

In this paper, we propose a new motion estimation algorithm for frame rate up-conversion. The proposed algorithm uses the variance of errors in addition to SAD in motion estimation to find more accurate motion vectors. Then, it decides which motion vectors are wrong using the variance of neighbor motion vectors and the variance between current motion vector and neighbor's average motion vector. Next, incorrect motion vectors are corrected by weighted sum of eight neighbor motion vectors. Additionally, we propose adaptive search range algorithm, so we can find more accurate motion vectors and reduce computational complexity at the same time. As a result, proposed algorithm improves the average peak signal-to-noise ratio and structural similarity up to 1.44 dB and 0.129, respectively, compared with previous algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.228-230
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• 2016

In this paper, we introduce new FRUC method for Multi-View Video based on DIBR (Depth Image based Rendering, DIBR). In the proposed method, we divide each block to sub-regions using depth map. Then, we reconstruct occlusion region information at each sub-regions by using other views. With reconstructed occlusion region information, we estimate and compensate each sub-regions' motion. The proposed method estimates more accurate motion compared to the conventional methods in occlusion region.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.947-949
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• 2015

본 논문은 기존의 시간적으로 이웃한 프레임 사이의 움직임을 추정 보상하여 새로운 프레임을 생성하는 프레임률 향상 기법 (frame rate up conversion)을 제안한다. 움직임 추정(Motion Estimation)을 통하여 계산된 움직임 벡터를 이용하여 프레임을 생성하며, 생성된 프레임에서 발생되는 구명 (hole)과 중첩 (overlap) 영역을 처리하는 기법을 제안한다. 특히 k-NN 보간법(k-nearest neighbor interpolation)[3]과 중간값을 적응적으로 활용하여 향상된 화질의 영상을 생성한다. 실험 결과를 통하여 제안하는 기술의 우수성을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1520-1522
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• 2015

본 논문은 기존의 시간적으로 이웃한 프레임 사이의 움직임을 추정 보상하여 새로운 프레임을 생성하는 프레임률 향상 기법 (frame rate up conversion)을 제안한다. 움직임 추정(Motion Estimation)을 통하여 계산된 움직임 벡터를 이용하여 프레임을 생성하며, 생성된 프레임에서 발생되는 구멍 (hole)과 중첩 (overlap) 영역을 처리하는 기법을 제안한다. 특히 k-NN 보간법(k-nearest neighbor interpolation)[3]과 중간값을 적응적으로 활용하여 향상된 화질의 영상을 생성한다. 실험 결과를 통하여 제안하는 기술의 우수성을 입증하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.650-652
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• 2022

Frame rate up-conversion (FRUC) algorithm is an image interpolation technology that improves the frame rate of moving pictures. This solves problems such as screen shake or blurry motion caused by low frame rate video in high-definition digital video systems, and provides viewers with a more free and smooth visual experience. In this paper, we propose a video compression technique using deep learning-based FRUC algorithm. The proposed method compresses and transmits after excluding some images from the original video, and uses a deep learning-based interpolation method in the decoding process to restore the excluded images, thereby compressing them with high efficiency. In the experiment, the compression performance was evaluated using the decoded image and the image restored by the FRUC algorithm after encoding the video by skipping 1 or 3 pages. When 1 and 3 sheets were excluded, the average BD-rate decreased by 81.22% and 27.80%. The reason that excluding three images has lower encoding efficiency than excluding one is because the PSNR of the image reconstructed by the FRUC method is low.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.28-36
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• 2009

In this paper, we propose a frame-rate up-conversion method for temporal quality enhancement. The proposed method adaptively changes search range during hierarchical motion estimation and reconstructs hole regions using the proposed bi-direction prediction and linear interpolation. In order to alleviate errors due to inaccurate motion vector estimation, search range is adaptively changed based on reliability and for more accurate, motion estimation is performed in descending order of block variance. After segmentation of background and object regions, for filling hole regions, the pixel values of background regions are reconstructed using linear interpolation and those of object regions are compensated based on the proposed hi-directional prediction. The proposed algorithm is evaluated in terms of PSNR with original uncompressed sequences. Experimental results show that the proposed algorithm is better than conventional methods by around 2dB, and blocky artifacts and blur artifacts are significantly diminished.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.532-536
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• 2016

Frame Rate Up Conversion (FRUC) generate interpolated frames between existing frames using motion estimation and motion compensation interpolation considering temporal redundancy. Falsely-estimated FRUC, however, can generate poor quality frames because FRUC typically uses blending-based interpolation method. As skipping an interpolating process between frames generate mis-estimated motion vectors, could improve Quality of Services of FRUC. In this Paper we analyze luminance histogram and motion vector consistency in frames generating poor quality interpolated frames. We conclude these features could help to be a clue in classifying the frames, which often result in the poor quality of FRUC results through the analysis and experiment.