• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.313-320
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• 2017

The In-loop filtering methods such as de-blocking filter and SAO(Sample Adaptive Offset) applied to the HEVC standard achieves coding efficiency and subjective quality improvement by reducing the blocking artifacts and the ringing artifacts. However, despite the use of In-loop filtering methods, the artifacts called a corner outlier occurring at the corner points of block boundaries are not removed. In this paper, the corner outlier artifacts are reduced by the detection, determination, and filtering processes on the corner outlier pixels. Experimental results show that the proposed method improves the subjective picture quality and slightly increases the coding efficiency in Inter prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.691-702
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• 2013

In this study the quantitative outlier-filtering algorithm has been developed using the smoothing method based on the day-of-the-week traffic volume variation pattern and then, in order to test the effectiveness of the algorithm, it has been used to identify outliers from the traffic volume data collected at 14 continuous traffic counts sites on the national highways in the year 2010. The test results are satisfactory since the filtering rate is 98.2% for normal days and the mis-filtering rate is 8.0% for abnormal days. Therefore, the algorithm will be able to be used for roughly-but-quickly filtering outliers from the collected traffic volume data.

• The Journal of the Acoustical Society of Korea
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• v.17 no.3E
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• pp.47-52
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• 1998

This paper prolposes a VFM (Variable Fading Memory)Kalman filtering and applies it to the parameter estimation for time-varying signals. By adaptively calculating the fading memory, the proposed algorithm does not require any predetermined fading memory when estimating the time-varying signal parameter. Moreover, the proposed algorithm has faster convergence speed than fixed fading memory one in case the signal contains an impulsive outlier. The performance of parameter estimation for time-varying signal is evaluated by computer simulation for two cases, one of which is the chirp signal whose frequency varies linearly with time and the other is the chip signal with an impulsive outlier. The experimental results show that the VFM Kalman filtering estimates the parameter of the chirp signal more rapidly than the fixed fading memory one in the region of an outlier.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.334-339
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• 2019

In this study, we propose a compensation method of raw LiDAR data with noise and noise filtering for signal processing of LiDAR sensors during the development phase. The raw LiDAR data include constant errors generated by delays in transmitting and receiving signals, which can be resolved by LiDAR signal compensation. The signal compensation consists of two stage. First one is LiDAR sensor calibration for a compensation of geometric distortion. Second is walk error compensation. LiDAR data also include fluctuation and outlier noise, the latter of which is removed by data filtering. In this study, we compensate for the fluctuation by using the Kalman filter method, and we remove the outlier noise by applying a Gaussian weight function.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.241-250
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• 2008

With the ever-growing amount of traffic, there is an increasing need for good quality travel time information. Various existing outlier filtering and missing data imputation algorithms using AVI data for interrupted and uninterrupted traffic flow have been proposed. This paper is devoted to development of an outlier filtering and missing data imputation algorithm by using Toll Collection System (TCS) data. TCS travel time data collected from August to September 2007 were employed. Travel time data from TCS are made out of records of every passing vehicle; these data have potential for providing real-time travel time information. However, the authors found that as the distance between entry tollgates and exit tollgates increases, the variance of travel time also increases. Also, time gaps appeared in the case of long distances between tollgates. Finally, the authors propose a new method for making representative values after removal of abnormal and "noise" data and after analyzing existing methods. The proposed algorithm is effective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3815-3835
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• 2022

Density-based outlier detection is one of the hot issues in data mining. A point is determined as outlier on basis of the density of points near them. The existing density-based detection algorithms have high time complexity, in order to reduce the time complexity, a new outlier detection algorithm DODMD (Density-based Outlier Detection in Multidimensional Datasets) is proposed. Firstly, on the basis of ZH-tree, the concept of micro-cluster is introduced. Each leaf node is regarded as a micro-cluster, and the micro-cluster is calculated to achieve the purpose of batch filtering. In order to obtain n sets of approximate outliers quickly, a greedy method is used to calculate the boundary of LOF and mark the minimum value as LOF_min. Secondly, the outliers can filtered out by LOF_min, the real outliers are calculated, and then the result set is updated to make the boundary closer. Finally, the accuracy and efficiency of DODMD algorithm are verified on real dataset and synthetic dataset respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.217-223
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• 2004

In block-based lossy video compression, severe quantization causes discontinuities along block boundaries so that annoying blocking artifacts are visible in decoded video images. These blocking artifacts significantly decrease the subjective image quality. In order to reduce the blocking artifacts in decoded images, many algorithms have been proposed. However studies on so called comer outlier, have been very limited. Corner outliers make image edges look disconnected from those of neighboring blocks at cross block boundary. In order to solve this problem we propose a corner outlier detection and compensation algorithm as loop-filtering in spatial domain. Experiment results show that the proposed method provides much improved subjective image quality.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.305-317
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• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.