• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.861-872
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• 2011

Many fast block-matching algorithms (BMAs) in motion estimation field reduce computational complexity by screening the number of checking points. Although many fast BMAs reduce computations, sometimes they should endure matching errors in comparison with full-search algorithm (FSA). In this paper, a novel fast BMA for constrained one-bit transform (C1BT)-based motion estimation is proposed in order to decrease the calculations of the block distortion measure. Unlike the classical fast BMAs, the proposed algorithm shows a new approach to reduce computations. It utilizes the binomial distribution based on the characteristic of binary plane which is composed of only two elements: 0 and 1. Experimental results show that the proposed algorithm keeps its peak signal-to-noise ratio (PSNR) performance very close to the FSA-C1BT while the computation complexity is reduced considerably.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.100-106
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• 2015

In this paper, we propose a fast algorithm for low-power image enhancement method for mobile LCD. In the proposed fast algorithm, the spatial resolution of the input image is significantly reduced, and the image characteristics are analyzed on the reduced resolution image to find a dimming rate adaptive to the image content, thereby saving power. The proposed fast adaptive dimming and image enhancement algorithm is implemented as an application that runs on an Android device. Image quality evaluation and running time analysis experiments on the device indicate that the proposed fast algorithm jointly minimizes the quality degradation and power consumption, reducing the required computation load by over 95%.

• 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%.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.75-81
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• 2004

Gaussian selection (GS) is a popular approach in the continuous density hidden Markov model for fast decoding. It enables fast likelihood computation by reducing the number of Gaussian components calculated. In this paper, we propose a new GS method for the phonetic tied-mixture (PTM) hidden Markov models. The PTM model can represent each state of the same topological location with a shared set of Gaussian mixture components and contort dependent weights. Thus the proposed method imposes constraint on the weights as well as the number of Gaussian components to reduce the computational load. Experimental results show that the proposed method reduces the percentage of Gaussian computation to 16.41%, compared with 20-30% for the conventional GS methods, with little degradation in recognition.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.31-39
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• 2010

The Generalized Hough Transform(GHT) is the most used algorithm for circle detection with high accuracy. However, it requires many computation time, because many different templates are applied in order to find circles of various size. In the case of circle detection and tracking in video, the classical approach applies GHT for each frame in video and thus needs much high processing time for all frames. This paper proposes the fast GHT algorithm in video, using two consecutive frames are similar. In the proposed algorithm, a change-driven method conducts GHT only when two consecutive frames have many changes, and trajectory-based method does GHT in candidate areas and with candidate radius using circles detected in a previous frame. The algorithm can reduce computation time by reducing the number of frames, the edge count, and the number of searching circles, as factors which affects the speed of GHT. Our experimental results show that the algorithm successfully detects circles with less processing time and no loss of accuracy in video acquisited by a fixed camera and a moving camera.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1589-1596
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• 2010

In this paper, we propose the method that reduce computational complexity for intermode decision using CBP(coded block pattern) and coded information of colocated-MB(macro block). Proposed method classifies MB into best-CBP and normal-CBP according to the characteristics of CBP. On best-CBP, it eliminates the computation for $8{\times}8$ mode on intermode decision process because the probability for SKIP mode and M-Type mode is 96.3% statistically. On normal-CBP, it selectively eliminates the amount of computation for bit-rate distortion cost, because it uses coded information of colocated-MB and motion vector cost in deciding SKIP mode and M-Type mode. The simulation results show that the proposed method reduces total coding time to 58.44% in average, and is effective in reducing computational burden in videos with little motion.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.106-114
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• 2009

In this paper we proposed a fast method for a K-Means Clustering algorithm. The main characteristic of this method is that it uses precalculated data which possibility of change is high in order to speed up the algorithm. When calculating distance to cluster centre at each stage to assign nearest prototype in the clustering algorithm, it could reduce overall computation time by selecting only those data with possibility of change in cluster is high. Calculation time is reduced by using the distance information produced by K-Means algorithm when computing expected input data whose cluster may change, and by using such distance information the algorithm could be less affected by the number of dimensions. The proposed method was compared with original K-Means method - Lloyd's and the improved method KMHybrid. We show that our proposed method significantly outperforms in computation speed than Lloyd's and KMHybrid when using large size data which has large amount of data, great many dimensions and large number of clusters.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.59-66
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• 2004

In this paper, a fast 3D model extraction algorithm with an enhanced PBIL of preserving depth consistency is proposed for the extraction of 3D depth information from 2D images. Evolutionary computation algorithms are efficient search methods based on natural selection and population genetics. 2D disparity maps acquired by conventional matching algorithms do not match well with the original image profile in disparity edge regions because of the loss of fine and precise information in the regions. Therefore, in order to decrease the imprecision of disparity values and increase the quality of matching, a compact genetic algorithm is adapted for matching environments, and the adaptive window, which is controlled by the complexity of neighbor disparities in an abrupt disparity point is used. As the result, the proposed algorithm showed more correct and precise disparities were obtained than those by conventional matching methods with relaxation scheme.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.1
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• pp.30-37
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• 2011

In this paper, we propose a fast ICI suppression algorithm with adaptive channel estimation for the LTE-Advanced uplink system. In order to effectively remove phase noise and carrier frequency offset at time varying channel, we use the comb type pilot. The purpose is to improve performance by reducing computational complexity than conventional PNFS(Phase Noise & Frequency offset Suppression) algorithm. We reduce computational complexity by decreasing overlapping computation or unnecessary computation at conventional PNFS algorithm. Also, we propose an effective channel estimation method. We estimate and compensate multipath channel through the proposed adaptive channel estimation method. The BER performance of the proposed method is better about 0.5 dB than the conventional method at the Vehicular A channel.

• 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.