• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2015.07a
• /
• pp.334-336
• /
• 2015

In this paper, we describe a couple of methods for Frame Rate Up Conversion (FRUC) in Multi-View system. First, we introduce View Synthesis scheme that generates virtual views and FRUC that interpolates new frames between consecutive frames. Then, we introduce two applications of FRUC. One is a traditional application. The other is an application that uses feature of Multi-View system. Then we compare each result of two applications.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.6
• /
• pp.62-69
• /
• 2015

In this paper, a new frame rate up conversion (FRUC) algorithm using adaptive motion estimation (AME-FRUC) is proposed. The proposed algorithm performs extended bilateral motion estimation (EBME) conducts motion estimation (ME) processes on the static region, and extract region of interest with the motion vector (MV). In the region of interest block, the proposed AME-FRUC uses the texture block partitioning scheme and the unilateral motion estimation for improving ME accuracy. Finally, motion compensated frame interpolation (MCFI) are adopted to interpolate the intermediate frame in which MCFI is employed adaptively based on ME scheme. Experimental results show that the proposed algorithm improves the PSNR up to 3dB, the SSIM up to 0.07 and 68% lower SAD calculations compared to the EBME and the conventional FRUC algorithms.

• Journal of Broadcast Engineering
• /
• v.21 no.4
• /
• pp.472-483
• /
• 2016

This paper proposes a blocking artifacts detection algorithm in frequency domain for MC-FRUC (Motion Compensated Frame Rate Up-Conversion). Conventional MC-FRUC algorithms occur blocking artifacts near interpolated block boundaries since motion compensation is performed from block-based motion vector. For efficiently decreasing blocking artifacts, this paper analyses frequency characteristics of the interpolated frame and reduces blocking artifacts on block boundaries. In experimental results the proposed method shows better subjective quality than some conventional FRUC method and also increases the PSNR(Peak Signal to Noise Ratio) value on average 0.45 dB compared with BDMC(Bi-Directional Motion Compensation).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.927-929
• /
• 2011

Since DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based cost evaluation models are tested and can be effectively used for estimating the MSE noise.

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

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06a
• /
• pp.164-166
• /
• 2012

최근 스마트 기기의 보급과 무선 인터넷망의 보급으로 언제 어디에서나 비디오를 시청할 수 있다. 하지만 무선 인터넷 망의 품질이 안좋을 경우 영상의 QoS(Quality of Service)를 낮춰 프레임을 스킵하여 전송하게 된다. 이 때 FRUC(Frame Rate Up Conversion)기술을 적용한다면 원본의 프레임 레이트를 확보할 수 있어 QoS를 높일 것으로 기대한다. FRUC에서 MV(Motion Vector)추정시에 연산량이 매우 높아서 스마트 기기에 적용하는 것이 어렵지만 H.264코덱으로 인코딩된 동영상은 자체적으로 MV정보를 갖고 있기 때문에 이 MV를 FRUC에 적용할 수 있다면 FRUC의 연산량을 줄일 수 있을 것이다. 이를 위해서 H.264에 적용된 ME(Motion Estimation)와 FRUC에 적용된 ME의 차이를 고려하여 H.264코덱의 MV가 유용한지 분석하는 것이 선행돼야 한다. 본 논문에서는 H.264 MV와 FRUC의 MV의 차이를 분석하고 유용성을 판단하는 실험을 통해 H.264로 인코딩 된 비디오의 MV중 상당수가 FRUC에 적합함을 확인했다.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9C
• /
• pp.761-770
• /
• 2010

In this paper, we propose a new Frame Rate Up-Conversion (FRUC) scheme to increase the frame rate from a lower number into a higher one and enhance the decoded video quality at the decoder. The proposed algorithm utilizes the preliminary frames of forward and backward direction using bilateral prediction. In the process of the preliminary frames, an additional interpolation is performed for the occlusion area because if the calculated value of the block with reference frame if larger than the predetermine thresholdn the block is selected as the occlusion area. In order to interpolate the occlusion area, we perform re-search to obtain the osiomal block considerhe osiomnumber of available ne block consblock. The experimental results show that performance of the proposed algorithm has better PSNR and visual quality than the conventional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.10a
• /
• pp.867-869
• /
• 2012

FRUC(Frame Rate Up Conversion) is core techniques for higher quality terminals or low channel rate. For these applications, in this paper, we propose a block-based reliability evaluation method for FRUC techniques using bi-lateral symmetrical motion estimation. First, in order to measure the reliability, by introducing cost evaluation tools such as motion vector consistency as well as temporal matching and spatial matching characteristics and then by combining these tools, we propose an efficient method which arrange the blocks according to distortion size. Through several simulations, it is shown that the proposed method is very effective in finding the high distortion blocks. Furthermore, it is expected that the proposed method can be effectively utilized in FRUC techniques and Distributed Video Coding technologies.

• Journal of information and communication convergence engineering
• /
• v.9 no.2
• /
• pp.201-206
• /
• 2011

FRUC (Frame Rate Up-Conversion) technique needs an effective frame interpolation algorithm using motion information between adjacent neighboring frames. In order to have good visual qualities in the interpolated frames, it is necessary to develop an effective detection and interpolation algorithms for occluded regions. For this aim, this paper proposes an effective occluded region detection algorithm through the adaptive forward and backward motion searches and also by introducing the minimum value of normalized cross-correlation coefficient (NCCC). That is, the proposed scheme looks for the location with the minimum sum of absolute differences (SAD) and this value is compared to that of the location with the maximum value of NCCC based on the statistics of those relations. And, these results are compared with the size of motion vector and then the proposed algorithm decides whether the given block is the occluded region or not. Furthermore, once the occluded regions are classified, then this paper proposes an adaptive interpolation algorithm for occluded regions, which still exist in the merged frame, by using the neighboring pixel information and the available data in the occluded block. Computer simulations show that the proposed algorithm can effectively classify the occluded region, compared to the conventional SAD-based method and the performance of the proposed interpolation algorithm has better PSNR than the conventional algorithms.