• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.996-1008
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• 2011

This paper proposes a PSNR estimation algorithm of H.264/AVC bitstream for hierarchical B-picture structure. The proposed method consists of a modeling method for DCT coefficients for I-pictures and an error estimation method for blocks encoded by skip mode. The modeling method selects an appropriate model between Laplacian and Cauchy model, and the error of skip mode is estimated from MSE values of reference pictures. Experimental results show that the modeling method predicts more accurate PSNR values than Laplacian and Cauchy model and the error estimation method of skip mode enhances PSNR estimation of hierarchical B-picture structure.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.276-277
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• 2010

비디오 전송시스템에서 사용자가 시청하는 비디오의 화질을 측정하는 것은 중요한 작업이다. 압축된 비디오 스트림에서는 원본 영상이 없어 PSNR을 구할 수 없기 때문에 비디오 스트림 내 정보의 통계적 특성을 이용한 무참조(no-reference) PSNR 추정기법이 사용된다. 그러나 이 알고리즘은 인터 프레임에서의 성능이 매우 떨어지는 단점이 있기 때문에 신뢰도가 떨어지며 ML방법을 이용해 이 문제를 개선한 알고리즘이 있지만 복잡도가 증가하여 상용화에는 부적합하다. 본 논문에서는 이전 프레임의 PSNR과 인터 블록의 통계적 특성을 고려한 새로운 알고리즘을 제안하여 복잡도의 증가 없이 인터 프레임에서의 PSNR 추정 성능을 향상시켰다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.23-31
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• 2012

This paper proposes a motion estimation-based super resolution algorithm to restore input low-resolution images of large movement into a super-resolution image. It is difficult to find the sub-pixel motion estimation in images of large movement compared to typical experimental images. Also, it has disadvantage which have high computational complexity to find reference images and candidate images using general motion estimation method. In order to solve these problems for the traditional two-dimensional motion estimation using the proposed registration threshold that satisfy the conditions based on the reference image is determined. Candidate image with minimum weight among the best candidates for super resolution images, the restoration process to proceed with to find a new image registration algorithm is proposed. According to experimental results, the average PSNR of the proposed algorithm is 31.89dB and this is better than PSNR of traditional super-resolution algorithm and it also shows improvement of computational complexity.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.114-126
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• 2009

In this paper, we propose a PSNR(peak-to-peak signal to noise ratio) estimation method exploiting visual rhythm information for the reconstructed video frames at the customer's STB(Set-top Box). Key idea is that we can estimate the PSNR by using VR(visual rhythm) information even though a VR consists of the pixels in a vertical direction of a 2D(2-dimensional) video frame, because VR is the 1D projected version of a 2D video frame approximately. Simulation results show that the estimated PSNR from VR information is closely related to the PSNR from 2D video frames. The advantages of the proposed scheme includes that it can monitor the video quality efficiently while minimizing the computation load of STB, and show the location, duration and occurrence count of severe picture degradation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.70-79
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• 2007

H.264/AVC has many repeated computation for motion estimation. Because of that, it takes much time to encode and it is very hard to implement into a real-time encoder. Many fast algorithms were proposed to reduce computation time but encoding quality couldn't be qualified. In this paper we proposed a new motion vector prediction method for efficient and fast full search H.264/AVC motion estimation. We proposed independent motion vector prediction and SAD share for motion estimation. Using our algorithm, motion estimation reduce calculation complexity 80% and less distortion of image (less PSNR drop) than previous full search scheme. We simulated our proposed method. Maximum Y PSNR drop is about 0.04 dB and average bit increasing is about 0.6%.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.647-656
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• 2011

Motion estimation using one-bit transform and two-bit transform reduces the complexity for computation of matching error; however, the peak signal-to-noise ratio (PSNR) is degraded. Modified 1BT (M1BT) and modified 2BT (M2BT) have been proposed to compensate degraded PSNR by adding conditional local search. However, these algorithms require many additional search points in fast moving sequences with a block size of $16{\times}16$. This paper provides more efficient search method by preparing candidate blocks using the number of non-matching points (NNMP) than the conditional local search. With this NNMP-based search, we can easily obtain candidate blocks with small NNMP and efficiently search final motion vector. Experimental results show that the proposed algorithm not only reduces computational complexity, but also improves PSNR on average compared with conventional search algorithm used in M1BT, M2BT and AM2BT.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.69-72
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• 2008

본 논문에서는 PSNR 이 다른 어떤 척도보다도 화질의 평가에 있어서 중요하다는 전제하에 비쥬얼리듬 (VR) 정보를 이용하여 원본영상이 가용하지 않은 일반 시청자들의 STB 재생영상으로부터 PSNR 을 추정하는 방법을 제안하였다. VR 로부터 추정한 PSNR 값이 매 프레임 별로 2 차원 재생영상으로부터 구한 PSNR 과 같지는 않으나 시청자들이 평균적으로 어느 정도의 화질로 시청하고 있는지 비교적 정확하게 모니터링이 가능하다. 제안된 방법은 감소기준법(RR) 영상품질 측정방법으로 사용될 수 있다. STB 은 단순히 재생영상으로부터 VR 정보만 추출해서 서버로 전송하는 역할만 수행하면 되고, 나머지 연산은 품질관리 서버에서 수행하므로 자원이 부족한 STB 의 부담을 경감하면서도 효율적으로 품질을 모니터링할 수 있는 장점이 있다.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.167-170
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• 2000

In this paper, we present a fast algorithm for the motion estimation using the efficient selection of an initial search position. In the method, we select the initial search position using the motion vector from the subsmpled images, the predicted motion vector from the neighbor blocks, and the (0,0) motion vector. While searching the candidate blocks, we use the spiral search pattern with the successive elimination algorithm(SEA) and the partial distortion elimination(PDE). The experiment results show that the complexity of the proposed algorithm is about 2∼3 times faster than the three-step search(TSS) with the PSNR loss of just 0.05［dB］∼0.1［dB］ than the full search algorithm PSNR. The search complexity can be reduced with quite a few PSNR loss by controling the number of the depth in the spiral search pattern.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.787-797
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• 2008

In this paper, we propose a fast disparity estimation method considering temporal and spatial redundancy based on a dynamic programming for stereo matching. For the first step, the dynamic programming is performed to estimate disparity vectors with correlation between neighboring pixels in an image. Next, we efficiently compensate regions, which disparity vectors are not allocated, with neighboring disparity vectors assuming that disparity vectors in same object are quite similar. Moreover, in case of video sequence, we can decrease a complexity with temporal redundancy between neighboring frames. For performance comparison, we generate an intermediate-view image using the estimated disparity vector. Test results show that the proposed algorithm gives $0.8{\sim}2.4dB$-increased PSNR(peak signal to noise ratio) compared to a conventional block matching algorithm, and the proposed algorithm also gives approximately 0.1dB-increased PSNR and $48{\sim}68%$-lower complexity compared to the disparity estimation method based on general dynamic programming.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.