• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.275-282
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• 2004

In this paper, a transcoding method that transforms MPEG-4 video bitstream coded in 30 Hz frame rate into H.264 video bitstream of 15 Hz frame rate is proposed. The block modes and motion vectors in MPEG-4 is utilized in H.264 for block mode conversion and motion vector (MV) interpolation methods. The proposed three types of MV interpolation method can be used without performing full motion estimation in H.264. The proposed transcoder reduces computation amount for full motion estimation in H.264 and provides good quality of H.264 video at low bitrates. In experimental results, the proposed methods achieves 3.2-4 times improvement in computational complexity compared to the cascaded pixel-domain transcoding, while the PSNR (peak signal to noise ratio) is degraded with 0.2-0.9dB depending on video sizes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.341-349
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• 2014

Dynamic adaptive streaming over HTTP (DASH) is the most recent and promising technology to support high quality streaming services. In dynamic adaptive streaming over HTTP (DASH), a client consecutively estimates the available network bandwidth and decides the transmission rate for the forthcoming video chunks to be downloaded. In this paper, we propose a novel rate adaptation algorithm called quality of experience QoE-enhanced adaptation algorithm over DASH (QAAD), which preserves the minimum buffer length to avoid interruption and minimizes the video quality changes during the playback. We implemented a DASH test bed and conducted extensive experiments. Experimental results demonstrate that under fluctuating network conditions, QAAD provides seamless streaming with stabilized video quality while the previous buffer-aware algorithm (i.e., QDASH[9]) frequently changes the video quality and undergoes the interruption.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.991-999
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• 2010

In this paper, we propose a fast motion estimation algorithm for video encoding. Conventional fast motion estimation algorithms have a serious problem of low prediction quality in some frames. However, full search based fast algorithms have low computational reduction ratio. In the paper, we propose an algorithm that significantly reduces unnecessary computations, while keeping prediction quality almost similar to that of the full search. The proposed algorithm uses distribution probability of motion vectors and adaptive search patterns and block matching criteria. By taking different search patterns and error criteria of block matching according to distribution probability of motion vectors, we can reduces only unnecessary computations efficiently. Our algorithm takes only 20~30% in computational amount and has decreased prediction quality about 0~0.02dB compared with the fast full search of the H.264 reference software. Our algorithm will be useful to real-time video coding applications using MPEG-2 or MPEG-4 AVC standards.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1036-1043
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• 2005

In this paper, we propose a sequential motion vector recovery algorithm for H.264 video coding standard. Motion vectors of H.264 video coding standard cover relatively smaller areas than other standard, since motion estimation of H.264 takes place in the fashion of variable block size. Therefore, the correlation of motion vectors between neighboring blocks increases as the block size of motion estimation is lowered. Under the framework of sequential recovery, we introduce a motion vector recovery using $\alpha$-trimed mean filter. Experimental results show that proposed algorithm is useful in real time video delivery .with nearly comparable or better visual quality than previous approaches such as macro block boundary matching and Lagrage interpolation.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.441-449
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• 2007

In video coding, motion estimation is a process to estimate the pixel of the current frame from the reference frame, which affects directly the predictive quality and the encoding time. This paper is related to AHHS(Adaptive Hierarchical Hexagon Search) using spatio-temporal motion activity for fast motion estimation. The proposed method defines the spatio-temporal motion activity of the current macroblock using the motion vectors of its spatio-temporally adjacent macroblocks, and then conventional AHS(Adaptive Hexagon Search) is performed if the spatio-temporal motion activity is lower, otherwise, hierarchical hexagon search is performed on a multi-layered hierarchical space constructed by multiple sub-images with low frequency in wavelet transform. In the paper, based on computer simulation results for multiple video sequences with different motion characteristics, the performance of the proposed method was analysed and assessed in terms of the predictive quality and the computational time. Experimental results indicate that the proposed method is both suitable for (quasi-) stationary and large motion searches. The proposed method could keep the merit of the adaptive hexagon search capable of fast estimating motion vectors and also adaptively reduce the local minima occurred in the video sequences with higher spatio-temporal motion activity.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.62-67
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• 2017

In this paper, we propose a fast motion estimation algorithm which is important in performance of video encoding. Even though so many fast algorithms for motion estimation have been published due to tremendous computational amount of full search algorithm, efforts for reducing computations of motion estimation still remain. In the paper, we propose an algorithm that reduces unnecessary computations only, while keeping prediction quality the same as that of the full search. The proposed algorithm does not calculate block matching error for each candidate directly to find motion vectors but divides the calculation procedure into several steps and calculates partial sum of block errors for candidates with high priority. By doing that, we can find the minimum error point early and get the enhancement of calculation speed by reducing unnecessary computations. The proposed algorithm uses smaller computations than conventional fast search algorithms with the same prediction quality as the full search algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4587-4605
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• 2019

High Efficiency Video Coding (HEVC) suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes an adaptive search range decision algorithm for accelerating HEVC integer-pel ME on GPU which estimates the optimal search range (SR) using a MAP (Maximum A Posteriori) estimator. There are three main contributions; First, we define the motion feature as the standard deviation of motion vector difference values in a CTU. Second, a MAP estimator is proposed, which theoretically estimates the motion feature of the current CTU using the motion feature of a temporally adjacent CTU and its SR without any data dependency. Thus, the SR for the current CTU is parallelly determined. Finally, the values of the prior distribution and the likelihood for each discretized motion feature are computed in advance and stored at a look-up table to further save the computational complexity. Experimental results show in conventional HEVC test sequences that the proposed algorithm can achieves high average time reductions without any subjective quality loss as well as with little BD-bitrate increase.

• Journal of Broadcast Engineering
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• v.7 no.1
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• pp.55-65
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• 2002

Wavelet transform has recently emerged as a promising technique for video processing applications due to its flexibility in representing non-stationary video signals. Motion estimation which uses wavelet transform of octave band division method is applied In many places but if motion estimation error happens in the lowest frequency band. motion estimation error is accumulated by next time strep and there has the Problem that time and the data amount that are cost In calculation at each steps are increased. On the other hand. wavelet packet that achieved the best image quality in a given bit rate from a rate-distortion sense is suggested. But, this method has the disadvantage of time costs on designing wavelet packet. In order to solve this problem we solved this problem by introducing Top_down method. But we did not find the optimum solution in a given butt rate. That image variance can represent image complexity is considered in this paper. In this paper. we propose a fast multiresolution motion estimation scheme based on the adaptive wavelet transform for video compression.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.1
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• pp.79-85
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• 2010

In video standards such as H.264/AVC, motion estimation (ME) / compensation (MC) is regarded as a vital component in a video coder as it consumes a large amount of computation resources. The full search technique, which is used in general video codecs, gives the highest visual quality but also has the problem of significant computational load. To solve this problem, many fast algorithm has benn proposed. Among them, NPDS show that can maintain its video quality very close to the full search technique while achieving computation reduction by using a halfway-stop technique in the calculation of block distortion measure. In this paper, we proposed algorithm by determining minimum distortion measure with predictive motion vector and using the new search order. As the result, we can check that the proposed algorithm reduces the computational load 95% in average compared to the full search, respectively with the PSNR lost about 0.04dB.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.130-139
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• 2014

Undesired camera shaking can make a blur effect, which causes a degradation of video quality. We propose an efficient method of removing the blur effects on video captured from a single camera. The proposed method has a sequential process that is applied to each frame. The first stage is to estimate the camera motion for each frame. In order to estimate the camera motion, we compute the optical flow using 3 consecutive frames. Then a patch-wise image deconvolution is applied. During the deconvolution, edge prediction is used to improve the quality of image deconvolution. After patch-wise image deconvolution, deblurred patches are integrated into an image to produce a deblurred frame. The above process is performed for each frame. The experimental result shows that the proposed method removes the blur effect efficiently.