• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.101-108
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• 2009

The data broadcasting service, which is growing remarkably today, provides viewers with useful information as well as high quality video and audio. Service information is a kind of additional data that contains a wide range of information such as channel list and/or program title. Each service information is transmitted in the form of a table. And most standard committees have specified their own table list used for carrying the service information. Consequently, It causes incompatibility among services that each broadcast operators produce because the tables that they use differ from each other. In this paper, we propose a general-purpose service information processing system for an integrated data broadcasting middleware that is compatible with heterogenous broadcasting environments. The system is able to change its target table list dynamically without any code modification. Futhermore, we also adopted a thread pool model for efficient parsing and event dispatching.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.58-65
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• 2002

In order to reduce blocking artifacts in inter-frame coded images, we propose a new image restoration algorithm, which directly processes differential images before reconstruction. We note that blocking artifact in inter-frame coded images is caused by both 8$\times$8 DCT and 16$\times$16 macroblock based motion compensation, while that of intra-coded images is caused by 8$\times$8 DCT only. According to the observation, we Propose a new degradation model for differential images and the corresponding restoration algorithm that utilizes additional constraints and convex sets for discontinuity inside blocks. The proposed restoration algorithm is a modified version of standard regularization that incorporate!; spatially adaptive lowpass filtering with consideration of edge directions by utilizing a part of DCT coefficients. Most of video coding standard adopt a hybrid structure of block-based motion compensation and block discrete cosine transform (BDCT). By this reason, blocking artifacts are occurred on both block boundary and block interior For more complete removal of both kinds of blocking artifacts, the restored differential image must satisfy two constraints, such as, directional discontinuities on block boundary and block interior Those constraints have been used for defining convex sets for restoring differential images.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.11
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• pp.71-76
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• 2008

H.264(or MPEG-4/AVC pt.10) is a high performance video coding standard, and is widely used. Variable length code (VLC) of the H.264 standard compresses data using the statistical distribution of values. A decoder parses the compressed bit stream and searches decoded values in lookup tables, and the decoding process is not easy to implement by hardware. We propose an architecture of variable length decoder(VLD) for the H.264 baseline profile(BP) L4. The CAVLD decodes syntax elements using the combination of arithmetic units and lookup tables for the optimized hardware architecture. A barral shifter and a first 1's detector parse NAL bit stream, and are shared by Exp-Golomb decoder and CAVLD. A FIFO memory between CAVLD and the reorder unit and a buffer at the output of the reorder unit eliminate the bottleneck of data stream. The proposed VLD is designed using Verilog-HDL and is implemented using an FPGA. The synthesis result using a 0.18um standard CMOS technology shows that the gate count is 22,604 and the decoder can process HD($1920{\times}1080$) video at 120MHz.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.