• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.513-526
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• 2010

In this paper, we propose a de-blocking filter for improvement of coding efficiency and computational complexity reduction on a high definition video coding. Recently, the H.264/AVC standard-based research for high definition video coding method is under way because the amount of used of high definition videos is on the increase. The H.264/AVC de-blocking filter is designed for low bitrate video coding and it improves not only the subjective quality but also coding efficiency by minimizing the blocking artifact. However, the H.264/AVC de-blocking filter that strong filtering is performed is not suitable in a high definition video coding which occurs relatively low blocking artifact. Also, the conventional de-blocking filter has high computational complexity in decoder side. The computational complexity of the proposed method is reduced about maximum 8.8% than conventional method. Furthermore, the coding efficiency of the proposed method is about maximum 7.3% better than H.264/AVC de-blocking filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.460-468
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• 2009

Multiple-input multiple-output (MIMO) technology applied with orthogonal frequency division multiplexing (OFDM) is considered as the ultimate solution to increase channel capacity without any additional spectral resources. At the receiver side, the challenge resides in designing low complexity detection algorithms capable of separating independent streams sent simultaneously from different antennas. In this paper, we introduce an upper-lower bounded-complexity QRD-M algorithm (ULBC QRD-M). In the proposed algorithm we solve the problem of high extreme complexity of the conventional sphere decoding by fixing the upper bound complexity to that of the conventional QRD-M. On the other hand, ULBC QRD-M intelligently cancels all unnecessary hypotheses to achieve very low computational requirements. Analyses and simulation results show that the proposed algorithm achieves the performance of conventional QRD-M with only 26% of the required computations.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.1-13
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• 2011

In this paper, we propose an adaptive in-loop filter to improve the coding efficiency. Recently, there are post-filter hint SEI and block-based adaptive filter control (BAFC) methods based on the Wiener filter which can minimize the mean square error between the input image and the decoded image in video coding standards. However, since the post-filter hint SEI is applied only to the output image, it cannot reduce the prediction errors of the subsequent frames. Because BAFC is also conducted with a deblocking filter, independently, it has a problem of high computational complexity on the encoder and decoder sides. In this paper, we propose the low-complexity adaptive in-loop filter (LCALF) which has lower computational complexity by using H.264/AVC deblocking filter, adaptively, as well as shows better performance than the conventional method. In the experimental results, the computational complexity of the proposed method is reduced about 22% than the conventional method. Furthermore, the coding efficiency of the proposed method is about 1% better than the BAFC.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.977-984
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• 2019

This paper proposes a single image dehazing that utilizes the transmission estimation with low complexity and the pixel-based JBDCP (Joint Bright and Dark Channel Prior) for the effective application of hazy outdoor images. The conventional transmission estimation includes the refinement process with high computational complexity and memory requirements. We propose the transmission estimation using combination of pixel- and block-based dark channel information and it significantly reduces the complexity while preserving the edge information accurately. Moreover, it is possible to estimate the transmission reflecting the image characteristics, by obtaining a different air-light for each pixel position of the image using the pixel-based JBDCP. Experimental results on various hazy images illustrate that the proposed method exhibits excellent dehazing performance with low complexity compared to the conventional methods; thus, it can be applied in various fields including real-time devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1249-1254
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• 2016

Recently, there have been many researches which can achieve high data rate in multi-user massive MIMO networks while reducing the complexity in terms of both hardware and algorithm. In addition, many researches have been conduced to reduce the energy consumption in next generation mobile communication networks. In this paper, we thus investigated new transmit antenna selection scheme to achieve low computational complexity and enhance energy efficiency in multi-user massive MIMO networks. First, we introduced the optimal scheme based on Brute-Force searching to maximize the energy efficiency and then proposed new antenna selection scheme to dramatically reduce the computational complexity compared to the optimal scheme. As the number of transmit antennas increases, the complexity of the optimal scheme exponentially increases while the complexity of the proposed scheme linearly increases. Nevertheless, the energy efficiency performance gap between proposed and optimal schemes is not huge.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6C
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• pp.539-546
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• 2010

In next generation wireless communication systems based on OFDM, multiple-input multiple-output (MIMO) technique is adopted in order to achieve high data throughput with limited bandwidth. As one of MIMO techniques, spatial multiplexing scheme needs high performance data detection algorithm that can be performed with low computational complexity. In this paper, we propose an algorithm that can compute QRM-MLD with reduced complexity. Also, hybrid detection technique is proposed, which can reduce the complexity by selecting between MMSE and QRM-MLD according to the channel condition. The proposed algorithm provides the trade-off between performance and complexity. The computer simulations for downlink transmission in 3GPP LTE system show that less than 0.1dB performance degradation can be achieved at 0.1% BER with 59% reduction on computational complexity compared with the conventional QRM-MLD algorithm.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.151-154
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• 2006

In this work, a novel optimal multiuser detection (MUD) approach, which not only achieves the optimal maximum-likelihood (ML)-like performance but also has reasonably low computational complexity, for Space-time coded OFDM (ST-OFDM) systems is presented. In the proposed detection scheme, the signal model is firstly re-expressed into linearly equivalent one. Then, with the linearly equivalent signal model, a new jointly MUD algorithm is proposed to detect signals. The ML-like bit-error-rate (BER) performance and reasonably low complexity of the proposed detection are verified by computer simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.675-679
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• 2018

A novel heart rate estimation algorithm is presented based on normalized least-mean-square (NLMS) algorithm. This paper presented a three-step processing scheme for estimating heart rate from PPG signal with motion artifacts. The proposed active noise cancellation algorithm has low computational complexity compared to the NLMS algorithm. Experimental results show that the proposed algorithms perform similar with the previous algorithm under motion artifact noises.