• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.1020-1026
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• 2016

Motion estimation (ME) algorithms supporting quarter-pixel accuracy have been recently introduced to retain detailed motion information for high quality of video in the state-of-the-art video compression standard of H.264/AVC. Conventional sub-pixel ME algorithms in the spatial domain are faced with a common problem of computational complexity because of embedded interpolation schemes. This paper proposes a low-complexity sub-pixel motion estimation algorithm in the transform domain utilizing shifting matrix. Simulations are performed to compare the performances of spatial-domain ME algorithms and transform-domain ME algorithms in terms of peak signal-to-noise ratio (PSNR) and the number of bits per frame. Simulation results confirm that the transform-domain approach not only improves the video quality and the compression efficiency, but also remarkably alleviates the computational complexity, compared to the spatial-domain approach.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.2035-2038
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• 2002

The necessity of the filter coefficients compensation for the LTJ adaptive filter was explained generally and easily by analyzing it with respect to the time-varying transform domain adaptive filter. And also the reduction method of computational complexity for filter coefficients compensation was proposed and its effectiveness was verified through experiments using artificial and real speech signals. The proposed adaptive filter reduces the computational complexity for filter coefficients compensation by 95%, and when the filter is applied to the acoustic echo canceller with 1000 taps, the total complexity is reduced by 82%

• ETRI Journal
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• v.27 no.5
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• pp.639-642
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• 2005

This paper proposes two kinds of complexity-reduced algorithms for a low density parity check (LDPC) decoder. First, sequential decoding using a partial group is proposed. It has the same hardware complexity and requires a fewer number of iterations with little performance loss. The amount of performance loss can be determined by the designer, based on a tradeoff with the desired reduction in complexity. Second, an early detection method for reducing the computational complexity is proposed. Using a confidence criterion, some bit nodes and check node edges are detected early on during decoding. Once the edges are detected, no further iteration is required; thus early detection reduces the computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1195-1211
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• 2016

The emerging video coding standard High Efficiency Video Coding (HEVC) has shown almost 40% bit-rate reduction over the state-of-the-art Advanced Video Coding (AVC) standard but at about 40% computational complexity overhead. The main reason for HEVC computational complexity is the inter prediction that accounts for 60%-70% of the whole encoding time. In this paper, we propose several fast coding unit (CU) encoding schemes based on the Merge mode and motion estimation information to reduce the computational complexity caused by the HEVC inter prediction. Firstly, an early Merge mode decision method based on motion estimation (EMD) is proposed for each CU size. Then, a Merge mode based early termination method (MET) is developed to determine the CU size at an early stage. To provide a better balance between computational complexity and coding efficiency, several fast CU encoding schemes are surveyed according to the rate-distortion-complexity characteristics of EMD and MET methods as a function of CU sizes. These fast CU encoding schemes can be seamlessly incorporated in the existing control structures of the HEVC encoder without limiting its potential parallelization and hardware acceleration. Experimental results demonstrate that the proposed schemes achieve 19%-46% computational complexity reduction over the HEVC test model reference software, HM 16.4, at a cost of 0.2%-2.4% bit-rate increases under the random access coding configuration. The respective values under the low-delay B coding configuration are 17%-43% and 0.1%-1.2%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.3
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• pp.21-30
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• 2013

Powell-Miller theory is a good method to express or treat incorrect information. But it has limitation that requires too much time to apply to actual situation because computational complexity increases in exponential and functional way. Accordingly, there have been several attempts to reduce computational complexity but side effect followed - certainty factor fell. This study suggested expanded Approximation Algorithm. Expanded Approximation Algorithm is a method to consider both smallest supersets and largest subsets to expand basic space into a space including inverse set and to reduce Approximation error. By using expanded Approximation Algorithm suggested in the study, basic probability assignment function value of subsets was alloted and added to basic probability assignment function value of sets related to the subsets. This made subsets newly created become Approximation more efficiently. As a result, it could be known that certain function value which is based on basic probability assignment function is closely near actual optimal result. And certainty in correctness can be obtained while computational complexity could be reduced. by using Algorithm suggested in the study, exact information necessary for a system can be obtained.

• ETRI Journal
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• v.40 no.2
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• pp.218-226
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• 2018

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.

• Journal of Information Processing Systems
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• v.2 no.3 s.4
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• pp.153-158
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• 2006

In this paper, we propose an image interpolator for low computational complexity. The proposed image interpolator supports the image scaling using a modified cubic convolution interpolation between the input and output resolutions for a full screen display. In order to reduce the computational complexity, we use the difference in value of the adjacent pixels for selecting interpolation methods and linear function of the cubic convolution. The proposed image interpolator is compared with the conventional one for the computational complexity and image quality. The proposed image interpolator has been designed and verified by Verilog HDL(Hardware Description Language). It has been synthesized using the Xilinx VirtexE FPGA, and implemented using an FPGA-based prototype board.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.1259-1262
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• 2006

In this paper, we propose a modified cubic convolution interpolation for the enlargement or reduction of digital images using a pixel difference value. The proposed method has a low complexity: the number of multiplier of weighted value to calculate one pixel of a scaled image has seven less than that of cubic convolution interpolation has sixteen. We use the linear function of the cubic convolution and the difference pixel value for selecting interpolation methods. The proposed method is compared with the conventional one for the computational complexity and the image quality. The simulation results show that the proposed method has less computational complexity than one of the cubic convolution interpolation.

• ETRI Journal
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• v.45 no.4
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• pp.666-677
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• 2023

In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side. To reduce the computational complexity of CNNs for edge computing, a lightweight algorithm, such as a MobileNet, is proposed. Although MobileNet is lighter than other CNN models, it commonly achieves lower classification accuracy. Hence, to find a balance between complexity and accuracy, additional hyperparameters for adjusting the size of the model have recently been proposed. However, significantly increasing the number of parameters makes models dense and unsuitable for devices with limited computational resources. In this study, we propose a novel MobileNet architecture, in which the number of parameters is adaptively increased according to the importance of feature maps. We show that our proposed network achieves better classification accuracy with fewer parameters than the conventional MobileNet.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11A
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• pp.1027-1037
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• 2010

In this paper, we propose the linear interpolation-based BD (Block Diagonalization) precoding approximation algorithm for low complexity in downlink multiuser MIMO-OFDM (Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing) systems. In the case of applying the general BD precoding algorithm to multiuser MIMO-OFDM systems, the computational complexity increases in proportional to the number of subcarriers. The proposed interpolation-based BD precoding approximation algorithm can be achieved similar SER performance with general BD algorithm and can decrease the computational complexity. It is proved that proposed algorithm can achieve the significantly decreased computational complexity by computer simulation.