• Journal of Information Processing Systems
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• v.16 no.4
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• pp.959-974
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• 2020

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.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1467-1477
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• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.

• Journal of the Korean association of regional geographers
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• v.20 no.2
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• pp.217-229
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• 2014

This study was performed to identify neighborhood deprivation indicators associated with health and to test the contextual effects of those indicators on individual health. This study calculated SMR based on Dong district and see the differences of prediction across deprivation index and indicators. Then, a multi-level analysis using HGLM was conducted to test the contextual effect of neighborhood depreivation indicators on health after controlling for demographic and socioeconomic status of individuals. The results showed that regional SMR had strong correlations with land price, education, welfare recipients, female household proportion in Dong district but failed to show the correlation with individual health and neighborhood deprivation. Individual health was only associated with individual level of demographic and socioeconomic status. That is, spatial dispersion of illness is understood as the distribution of social classes in terms of socioeconomic status of individuals, not the contextual aspects of community.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.2
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• pp.1-9
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• 2008

The objective of the study is 10 know the relation of landslide occurrence with using TPI (Topographic Position Index) in the Pyungchang County. Total 659 landslide scars were detected from aerial photographs. To analyze TPI, 100m SN (Small-Neighborhood) TPI map, 500m LN (Large-Neighborhood) TPI map, and slope map were generated from the DEM (Digital Elevation Model) data which are made from 1 : 5,000 digital topographic map. 10 classes clustered by regular condition after overlapping each TPI maps and slope map. Through this process, we could make landform classification map. Because it is only to classify landform, 7 classes were finally regrouped by the slope angle information of landslide occurrence detected from aerial photography analysis. The accuracy of reclassified map is about 46%.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.55-62
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• 2009

Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.170-176
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• 2010

Prediction is an important step in high-performance lossless data compression. In this paper, we propose a novel lossless image coding algorithm to increase prediction accuracy which can display low-resolution images quickly with a multi-resolution image technique. At each resolution, we use pixels of the previous resolution image to estimate current pixel values. For each pixel, we determine its estimated value by considering horizontal, vertical, diagonal edge information and average, weighted-average information obtained from its neighborhood pixels. In the experiment, we show that our method obtains better prediction than JPEG-LS or HINT.

• ETRI Journal
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• v.28 no.6
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• pp.721-731
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• 2006

A fast intra skip detection algorithm based on the ratedistortion (RD) cost for an inter frame (P-slices) is proposed for H.264/AVC video encoding. In the H.264/AVC coding standard, a robust rate-distortion optimization technique is used to select the best coding mode and reference frame for each macroblock (MB). There are three types of intra predictions according to profiles. These are $16{\times}16$ and $4{\times}4$ intra predictions for luminance and an $8{\times}8$ intra prediction for chroma. For the high profile, an $8{\times}8$ intra prediction has been added for luminance. The $4{\times}4$ prediction mode has 9 prediction directions with 4 directions for $16{\times}16$ and $8{\times}8$ luma, and $8{\times}8$ chrominance. In addition to the inter mode search procedure, an intra mode search causes a significant increase in the complexity and computational load for an inter frame. To reduce the computational load of the intra mode search at the inter frame, the RD costs of the neighborhood MBs for the current MB are used and we propose an adaptive thresholding scheme for the intra skip extraction. We verified the performance of the proposed scheme through comparative analysis of experimental results using joint model reference software. The overall encoding time was reduced up to 32% for the IPPP sequence type and 35% for the IBBPBBP sequence type.

• Management Science and Financial Engineering
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• v.16 no.2
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• pp.53-65
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• 2010

Despite the overall success of neighbor-based CF methods, there are some fundamental questions about neighbor selection and prediction mechanism including arbitrary similarity, over-fitting interpolation weights, no trust consideration between neighbours, etc. This paper proposes a simple method to compute absolute interpolation weights based on similarity values. In order to supplement the method, two schemes are additionally devised for high-quality neighbour selection and trust metrics based on co-ratings. The former requires that one or more neighbour's similarity should be better than a pre-specified level which is higher than the minimum level. The latter gives higher trust to neighbours that have more co-ratings. Experimental results show that the proposed method outperforms the pure IBCF by about 8% improvement. Furthermore, it can be easily combined with other predictors for achieving better prediction quality.

• Journal of the Korea Safety Management & Science
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• v.12 no.1
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• pp.127-136
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• 2010

In this paper, the author proposed following two methods to improve the accuracy of the recommender system. First, in order to classify the users more accurately, the author used a EMC(Expanded Moving Center) heuristic algorithm which improved clustering accuracy. Second, the author proposed the Neighborhood-oriented preference prediction method that improved the conventional preference prediction methods, so the accuracy of the recommender system is improved. The test result of the recommender system which adapted the above two methods suggested in this paper was improved the accuracy than the conventional recommendation methods.