Earticle

Many new techniques have been adopted in HEVC (High efficiency video coding) standard, such as quadtree-structured coding unit (CU), prediction unit (PU) partition, 35 intra-mode, and so on. To reduce computational complexity, the paper proposes two optimization algorithms which include fast CU depth range decision and fast PU partition mode decision. Firstly, depth range of CU is predicted according to spatial-temporal correlation. Secondly, we utilize the depth difference between the current CU and CU corresponding to the same position of adjacent frame for PU mode range selection. The number of traversal candidate modes is reduced. The experiment result shows the proposed algorithm obtains a lot of time reducing, and the loss of coding efficiency is inappreciable.

This paper introduces page-level rewrite interval prediction (PRWIP). By recording and analyzing the memory access history at page-level, we are able to predict the future memory accesses to any pages. Leveraging this information, this paper proposes a faster incremental checkpoint design by overlapping checkpoint phase with computing phase and thus achieves higher performance. Experimental results show that our new incremental checkpoint design can achieve averagely 22% speedup over traditional incremental checkpoint and 14% over the previous state-of-the-art work.

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

HEVC의 화면내 예측 부호화는 예측 모드를 기존 H.264/AVC에 비해서 최대 35개까지 확장함으로써 향상된 부호화 효율을 갖는다. 반면 화면내 부호화의 복잡도 또한 크게 증가하여 복잡도 감소를 위한 고속 부호화 기법이 요구된다. 본 논문에서는 HEVC의 참조모델인 HM에 화면내 고속 부호화를 위하여 채택된 RMD(Rough Mode Decision)에 추가적으로 복잡도 감소를 위하여 고속 화면내 예측 모드 결정 기법을 제시한다. 본 기법은 후보 모드를 결정하기 위한 RMD 과정에서의 탐색 모드 수와 선택된 후보 모드로부터 최종 모드를 결정하기 위한 탐색 모드 수를 제한하는 방법을 결합하여 모드 결정을 위한 복잡도를 감소한다. 본 제안 기법은 실험결과 HM 12.0에서 다양한 테스트시퀀스에서 HM 대비 1.0%의 비트 증가로 13.2%의 복잡도를 감소시킬 수 있었다.

Intra prediction in HEVC is also significantly improved from H.264/AVC with the increased prediction modes up to 35 that results in increase of the complexity cost. Hence, a fast intra mode decision algorithm is required for real-time applications. A fast intra mode decision is proposed in this paper for further complexity reduction in addition to the RMD (Rough Mode Decision) that has been adopted into the HEVC reference software, referred to as HM, for fast intra prediction. The proposed method reduces the complexity of intra mode decision by limiting the number of search modes in both steps of RMD and the final mode decision. Experimental results show that the proposed method provide about 13.2% encoding time reduction with 1.0% BD-rate increase on average over test sequences in HM 12.0.

As a widely used programming model for the purposes of processing large data sets, MapReduce (MR) becomes inevitable in data clusters or grids, e.g. a Hadoop environment. Load balancing as a key factor affecting the performance of map resource distribution, has recently gained high concerns to optimize. Current MR processes in the realization of distributed tasks to clusters use hashing with random modulo operations, which can lead to uneven data distribution and inclined loads, thereby obstruct the performance of the entire distribution system. In this paper, a virtual partition consistent hashing (VPCH) algorithm is proposed for the reduce stage of MR processes, in order to achieve such a trade-off on job allocation. Besides, experienced programmers are needed to decide the number of reducers used during the reduce phase of the MR, which makes the quality of MR scripts differ. So, an extreme learning method is employed to recommend potential number of reducer a mapped task needs. Execution time is also predicted for user to better arrange their tasks. According to the results, VPCH can lead to load balancing and our prediction model can provide fast prediction than SVM with similar accuracy maintained.

Data Stream is a continuous set of data records. When data arrive at a very high speed and continuously, so predicting the class in timely manner is important. Class prediction of data Stream is an important task in data mining. Nowadays Ensemble Modeling technique growing rapidly in Data Stream Classification. Ensemble learning become popular because of its advantage to handle large quantity of data stream, means it can handle the data in a bulk and also it can handle concept drifting. Earlier studies, mostly focused on accuracy of ensemble model, prediction efficiency has not considered much because existing ensemble model predicts in linear time, which is enough for general or small applications and existing models works on integrating small number of classifier. But real world application have large volume of data stream so we need more base classifier to identify different patterns and build a high grade ensemble model. To overcome these challenge we propose height balanced tree indexing structure (Ensemble tree) of base classifier for fast prediction on data streams by ensemble modeling technique. Ensemble Tree handles ensembles as spatial databases and it make use of an R-tree like structure to achieve sub linear time complexity.

As the extension of High Efficiency Video Coding (HEVC) for 3D video coding, 3D-HEVC achieves high efficiency for the compression of the multi-view videos plus depth (MVD) format. In order to ensure the performance of depth map coding, a new depth intra coding tool called Depth Modeling Mode (DMM) is introduced. However, the process of DMM significantly increases the computational complexity of depth map coding due to the blind traversal of all wedgelet partitions. In this paper, a fast intra prediction algorithm for DMM mode in depth map coding is proposed. In the first step, the unnecessary DMM mode is skipped by judging whether the best prediction mode in Rough Mode Decision (RMD) is DC mode. In the second step, the direction information represented by a permitted angle range is acquired based on the best prediction mode achieved in RMD. In the third step, a searching subset is obtained based on the direction information and the position information represented by the coordinates of the pixel with biggest depth value change in each boundary of PU. Then the patterns within the searching subset are tested by view synthesis optimization (VSO) to find the minimum distortion partition. Compared with the coarse-refinement algorithm, the proposed algorithm shows significant time saving with acceptable performance loss.

As one of the most important video compression technologies, inter prediction coding is highly efficient in reducing the temporal redundancy of video sequence. However, complicated inter prediction for the latest High Efficiency Video Coding standard (HEVC) brings high computational complexity and seriously restricts the encoding speed. In this paper, a fast inter prediction algorithm based on Rate-Distortion (RD) cost is proposed to improve inter prediction of HEVC. First, the splitting of Largest Coding Unit (LCU) is determined according to the RD costs with best Coding Unit (CU) size being 64x64 in the reference picture. Then, for other CUs in lower depths, the comparable RD costs are selected from encoded CUs in the same depth at the same Coding Tree Unit (CTU) based on the local homogeneity in spatial domain. By comparing the RD cost of current CU with its corresponding RD threshold, the splitting is terminated in advance. In this way, the proposed fast inter prediction algorithm can avoid the traversal of all CUs in the coding tree structure and improve the encoding speed. Experimental results show that the algorithm can save about 30% encoding time on the basis of ensuring visual quality and compression ratio of videos. Therefore, the computational complexity can be reduced greatly.

Signal processing takes an important role in genomic with enormous data available in public domain. Generally digital filters are applied to predict the protein and genes, but it needs to be redesigned when the characteristic frequency and periodic behavior is changed. In this paper proposed the novel adaptive algorithm which can identify the genes and proteins effectively from unified framework. First using the electron ion potential method the symbolic DNA sequences are converted in to digital signal. Secondly the filtering scheme for genomic signal processing with periodic behavior in biological sequence is introduced, which can predict and analyze the biological region that are interested in. finally the proposed adaptive filtering method is applied to recognize the exons of protein coding regions according to periodic-3 property. The exons prediction curves are obtained with Discrete Fourier Transform (DFT), Least mean square (LMS), and proposed Fast Recursive least Mean Square (F-RLS) algorithms. It is shown that proposed method shows efficiency in convergence of identification and precise prediction of exons regions compared to existed methods.

Flow induced vibration of wire-wrapped fuel rod in lead-bismuth reactor has become an increasing concern due to its significant influence on cladding fretting wear and reactor operation. In this study, we proposed a reduced order model to expedite the prediction of wire-wrapped fuel rod vibration. Firstly, the predefined structural motion is implemented in the fluid solver using a user defined function, while the fluid forces are computed. Subsequently, both the motion and fluid forces are decomposed into mode functions and time coefficients by dynamic mode decomposition. A long short-term memory network is then trained to bridge the time coefficients of motion and fluid forces. Finally, the vibration response of wire-wrapped fuel rod under different conditions is efficiently calculated using the Newmark integration scheme. The results demonstrate that 18 modes are sufficient to reconstruct the fluid forces, significantly reducing computation complexity, while the long short-term memory network provides reasonable fit to the original data. In addition, the study reveals that the motion of wire-wrapped fuel rod can be chaotic when the axial flow velocity exceeds 2.0 m/s. The mechanism of chaos is attributed to period-doubling bifurcation, a phenomenon not observed in prior simulations. Moreover, the asymmetric geometry of wire-wrapped fuel rod consistently leads to 'elliptic' trajectory, and the root mean square values of vibration amplitude can be fitted by the power function with respect to axial flow velocity. The proposed method offers a valuable and convenient tool to study the flow induced vibration of wire-wrapped fuel rod with significant efficiency improvement.

A novel fast algorithm is suggested for a coding unit (CU) mode decision using pseudo rate-distortion based on a separated encoding structure in High Efficiency Video Coding (HEVC). A conventional HEVC encoder requires a large computational time for a CU mode prediction because prediction and transformation procedures are applied to obtain a rate-distortion cost. Hence, for the practical application of HEVC encoding, it is necessary to significantly reduce the computational time of CU mode prediction. As described in this paper, under the proposed separated encoder structure, it is possible to decide the CU prediction mode without a full processing of the prediction and transformation to obtain a rate-distortion cost based on a suitable condition. Furthermore, to construct a suitable condition to improve the encoding speed, we employ a pseudo rate-distortion estimation based on a Hadamard transformation and a simple quantization. The experimental results show that the proposed method achieves a 38.68% reduction in the total encoding time with a similar coding performance to that of the HEVC reference model.

H.264는 가변 블록 크기, 다양한 방향성 예측 등과 같은 새로운 기법들을 도입하여 적은 비트율로 높은 압축효율을 가진다. 하지만 이와 같은 다양한 기법이 적용됨에 따라 계산복잡도가 크게 증가되었다. 본 논문에서는 계산복잡도를 줄이기 위해 효율적인 인트라 예측 모드 선택 기법을 제안한다. H.264의 $4{\times}4$ 인트라 예측을 위한 모드 선택에서는 8개의 방향성을 이용하는데 본 논문에서 제안하는 기법은 4개의 방향만을 사용하여 모드를 선택한 후, 선택한 모드의 방향성과 인접한 후보 모드를 추가하여 인트라 예측을 수행한다. 실험 결과, H.264 표준에서 제시한 $4{\times}4$ 인트라 모드 선택 기법과 비교하여 평균 83.7%의 정확도를 가지면서 계산복잡도는 63.3% 감소시켰다.

A fast intra-prediction method is proposed for High Efficiency Video Coding (HEVC) using a fast intra-mode decision and fast coding unit (CU) size decision. HEVC supports very sophisticated intra modes and a recursive quadtree-based CU structure. To provide a high coding efficiency, the mode and CU size are selected in a rate-distortion optimized manner. This causes a high computational complexity in the encoder, and, for practical applications, the complexity should be significantly reduced. In this paper, among the many predefined modes, the intra-prediction mode is chosen without rate-distortion optimization processes, instead using the difference between the minimum and second minimum of the rate-distortion cost estimation based on the Hadamard transform. The experiment results show that the proposed method achieves a 49.04% reduction in the intra-prediction time and a 32.74% reduction in the total encoding time with a nearly similar coding performance to that of HEVC test model 2.1.

In this paper, we propose a fast intra prediction mode selection method in Scalable Video Coding(SVC) which is an emerging video coding standard as an extension of H.264/Advanced Video Coding(H.264/AVC). The proposed method decides a candidate intra prediction mode based on the characteristic of macroblock smoothness. Statistical analysis is applied to computing that smoothness in spatial enhancement layer. We also propose an early termination scheme for Intra_BL mode decision where the RD cost value of Intra_BL is utilized. Compared with JSVM software, our scheme can reduce about 55% of the computation complexity of intra prediction on average, while the performance degradation is negligible; For low QP values, the average PSNR loss is very negligible, equivalently the bit rate increases by 0.01%. For high QP values, the average PSNR loss is less than 0.01dB, which equals to 0.25% increase in bitrate on average.

As the next-generation video coding standard, High Efficiency Video Coding (HEVC) has adopted advanced coding tools despite the increase in computational complexity. In this paper, we propose a selective bi-prediction method to reduce the encoding complexity of HEVC. The proposed method evaluates the statistical property of the sum of absolute differences in the motion estimation process and determines whether bi-prediction is performed. A performance comparison of the complexity reduction is provided to show the effectiveness of the proposed method compared to the HEVC test model version 4.0. On average, 50% of the bi-prediction time can be reduced by the proposed method, while maintaining a negligible bit increment and a minimal loss of image quality.

High Efficiency Video Coding (HEVC) is the most recent video coding standard to achieve a higher coding performance than the previous H.264/AVC. In order to accomplish this improved coding performance, HEVC adopted several advanced coding tools; however, these cause heavy computational complexity. Similar to previous video coding standards, motion estimation (ME) of HEVC requires the most computational complexity; this is because ME is conducted for three inter prediction modes - namely, uniprediction in list 0, uniprediction in list 1, and biprediction. In this paper, we propose an efficient inter prediction mode (EIPM) decision method to reduce the complexity of ME. The proposed EIPM method computes the priority of all inter prediction modes and performs ME only on a selected inter prediction mode. Experimental results show that the proposed method reduces computational complexity arising from ME by up to 51.76% and achieves near similar coding performance compared to HEVC test model version 10.1.

Precise prediction of the radiation interaction position in scintillators plays an important role in medical and industrial imaging systems. In this research, the incident position of the gamma rays was predicted precisely in a plastic rod scintillator by using attenuation technique and multilayer perceptron (MLP) neural network, for the first time. Also, this procedure was performed using nonlinear regression (NLR) method. The experimental setup is comprised of a plastic rod scintillator (BC400) coupled with two PMTs at two sides, a $^{60}Co$ gamma source and two counters that record count rates. Using two proposed techniques (ANN and NLR), the radiation interaction position was predicted in a plastic rod scintillator with a mean relative error percentage less than 4.6% and 14.6%, respectively. The mean absolute error was measured less than 2.5 and 5.5. The correlation coefficient was calculated 0.998 and 0.984, respectively. Also, the ANN technique was confirmed by leave-one-out (LOO) method with 1% error. These results presented the superiority of the ANN method in comparison with NLR and the other methods. The technique and set up used are simpler and faster than other the previous position sensitive detectors. Thus, the time, cost and shielding and electronics requirements are minimized and optimized.

본 논문은 모션 속도와 다수의 초기 중심점에 기반한 빠른 모션 추정 알고리즘을 제안한다. 제안한 방법은 시공간적 이웃 모션 벡터들에 의해 초기 탐색점을 예측한다. 모션벡터를 빠르게 구하기 위하여 모션 속도와 예측된 초기 중심점들에 기반한 동적인 탐색 패턴이 이용된다. 제안한 방법은 시공간 정보와 동적 탐색 패턴을 이용하여 탐색 속도를 매우 빠르게 할 뿐만 아니라 양질의 영상화질을 유지할 수 있다. 실험결과를 통해 제안한 방법이 완전 탐색, 새로운 삼단계 탐색, 사단계 탐색 방법들과 비교하여 검색시간을 줄이면서 PSNR 관점에서 양질의 영상화질을 제공함을 알 수 있다.

This paper proposes a fast motion estimation algorithm based on motion speed and multiple initial center points. The proposed method predicts initial search points by means of the spatio-temporal neighboring motion vectors. A dynamic search pattern based on the motion speed and the predicted initial center points is proposed to quickly obtain the motion vector. Due to the usage of the spatio-temporal information and the dynamic search pattern, the proposed method greatly accelerates the search speed while keeping a good predicted image quality. Experimental results show that the proposed method has a good predicted image quality in terms of PSNR with less searching time comparing with the Full Search, New Three-Step Search, and Four-Step Search.

ITU-T VCEG과 ISO/IEC MPEG은 공동으로 JCT-VC(Joint Collaborative Team on Video Coding) 를 구성하여 차세대 비디오 코덱 HEVC(High Efficiency Video Coding)에 대한 표준화를 진행하고 있다. 차세대 비디오 코덱 HEVC는 H.264/AVC 표준보다 높은 압축률을 보이나, 매우 높은 인코더 계산 복잡도를 가지고 있다. HEVC 인코더의 계산 복잡도를 줄이기 위해서 이 논문에서는 고속 예측단위 결정방법을 제안한다. 제안된 고속 예측단위 결정방법은 현재 prediction unit의 양자화 된 0이 아닌 변환계수가 없으면 남은 prediction unit의 부호화를 생략하여 부호화 시간을 줄이는 방법이다. 제안된 방법은 인코더 계산 복잡도를 HM6.0대비 약 50.3%정도 향상시키나 동일한 수준의 코딩 효율을 유지한다.

MPEG and VCEG have constituted a collaboration team called JCT-VC(Joint Collaborative Team on Video Coding) and have been developing the HEVC(High Efficiency Video Coding) standard. The next generation video coding standard HEVC shows higher compression rate compared with the H.264/AVC standard, but the encoder computational complexity of the HEVC encoder is significantly high. In order to reduce this computational complexity in the HEVC encoder, a fast prediction unit decision is proposed. The proposed fast prediction unit decision method reduces the encoder complexity by skipping the remaining prediction units if the current prediction unit does not have any non-zero quantized transform coefficient. The proposed method reduces the encoder computational complexity by 50.3% comparing with HM6.0 but it maintains the same level of coding efficiency.