• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.90-102
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• 2011

Recently, the grade of users using wireless communication services which transmits and re-transmits to the signal via the broadcasting satellite have a variety. However the ships not preparing of H.264 standard devices should not received the realtime data because the broadcasting stations have transmitted the compressed video data through the satellite communication. Therefore this paper proposes H.264 to MPEG-2 transcoding for the ships using MPEG-2 devices. Proposed method improves a speed and object quality in H.264 to MPEG-2 transcoding by analysis features of macroblock modes in H.264. In the Intra mode of P-frame, it proposes new method by computing coincidence proportion after comparing of Intra mode methods of H.264 and MPEG-2. In the Inter mode, it proposes a PMV(predictive motion vector) considering movement of motion vectors in H.264 decoder. we reuses a PMV directly as like the final MV in MPEG-2 encoder and refinements the MV after coincidence ratio comparing of variable motion vectors of H.264 and these of MPEG-2. The experimental results from proposed method show a considerable reduction in processing time, as much as 70% and 67% respectively, with a small objective quality reduction in PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2715-2720
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• 2014

Median filtering is very effective to remove impulse type noises, so it has been widely used in many signal processing applications. However, due to the time complexity of its non-linearity, median filtering is often used using a small filter window size. A lot of work has been done on devising fast median filtering algorithms, but most of them can be efficiently applied to input data with finite integer values like images. Little work has been carried out on fast 2-d median filtering algorithms that can deal with real-valued 2-d data. In this paper, a fast and simple median 2-d filter is presented, and its performance is compared with the Matlab's 2-d median filter and a heap-based 2-d median filter. The proposed algorithm is shown to be much faster than the Matlab's 2-d median filter and consistently faster than the heap-based algorithm that is much more complicated than the proposed one. Also, a more efficient median filtering scheme for 2-d real valued data with a finite range of values is presented that uses higher-bit integer 2-d median filtering with negligible quantization errors.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.79-89
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• 2023

A multi-view display is regarded as the most practical technology to provide a three-dimensional effect to a viewer because it can provide an appropriate viewpoint according to the observer's position. But, most multi-view displays with flat shapes have a disadvantage in that a viewer watches 3D images only within a limited front viewing angle. In this paper, we proposed a spherical display using a ball lens with spherical symmetry that provides perfect parallax by extending the viewing zone to 360 degrees. In the proposed system, each projection lens is designed to be packaged into a small modular array, and the module array is arranged in a spherical shape around a ball lens to provide vertical and horizontal parallax. Through the applied optical module, the image is formed in the center of the ball lens, and 3D contents are clearly imaged with the size of about 0.65 times the diameter of the ball lens when the viewer watches them within the viewing window. Therefore, the feasibility of a 360-degree full parallax display that overcomes the spherical aberration of a ball lens and provides a wide field of view is confirmed experimentally.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.