• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2109-2123
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• 2017

This paper presents a super-resolution reconstruction method based on sparse representation for two-dimensional bar code images. Considering the features of two-dimensional bar code images, Kirsch and LBP (local binary pattern) operators are used to extract the edge gradient and texture features. Feature extraction is constituted based on these two features and additional two second-order derivatives. By joint dictionary learning of the low-resolution and high-resolution image patch pairs, the sparse representation of corresponding patches is the same. In addition, the global constraint is exerted on the initial estimation of high-resolution image which makes the reconstructed result closer to the real one. The experimental results demonstrate the effectiveness of the proposed algorithm for two-dimensional bar code images by comparing with other reconstruction algorithms.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1495-1507
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• 1997

Among a number of approaches to text compression, adaptive dictionary schemes based on a sliding window have been very frequently used due to their high performance. The LZ77 algorithm is the most efficient algorithm which implements such adaptive schemes for the practical use of text compression. This paperpresents a VLSI architecture designed for processing the LZ77 algorithm in parallel. Compared with the other VLSI architectures developed so far, the proposed architecture provides the more viable solution to high performance with regard to its throughput, efficient implementation of the VLSI systolic arrays, and hardware scalability. Indeed, without being affected by the size of the sliding window, our system has the complexity of O(N) for both the compression and decompression and also requires small wafer area, where N is the size of the input text.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.157-168
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• 1993

In this paper, it is shown that neural networks trained with back propagation algorithm and dictionary can be applied to recognize handwritten English characters. To eliminate the useless data part and to minimize the variety of characters from the scanned image file, various preprocessings : that is, segmentation, centering, noise filtering, sealing and thinning are performed. After these, characteristic features are derived from thinned character pattern. The neural network is trained by using the extracted features for sample data, and all test data are classified into English alphabets according to their features through the neural network. Finally, the ways of reducing learning time and improving recognition rate, and the relationship between learning time and hidden layer nodes are considered. As a result of this study, after successful training, a high recognition rate has been obtained with this system for the trained patterns and about 93% for test patterns. Using dictionary, the recognition rate was about 97% for test pattern.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.608-620
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• 2021

Recent deep learning-based face super-resolution (FSR) works showed significant performances by utilizing facial prior knowledge such as facial landmark and dictionary that reflects structural or semantic characteristics of the human face. However, most of these methods require additional processing time and memory. To solve this issue, this paper propose an efficient FSR models using knowledge distillation techniques. The intermediate features of teacher network which contains dictionary information based on major face regions are transferred to the student through adversarial multi-scale features distillation. Experimental results show that the proposed model is superior to other SR methods, and its effectiveness compare to teacher model.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.227-239
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• 2014

This paper proposes an enhance prediction for conventional near-lossless coder to effectively lower external memory bandwidth in image processing SoC. First, we utilize an already reconstructed green component as a base of predictor of the other color component because high correlation between RGB color components usually exists. Next, we can improve prediction performance by applying variable block size prediction. Lastly, we use minimum internal memory and improve a temporal prediction performance by using a template dictionary that is sampled in previous frame. Experimental results show that the proposed algorithm shows better performance than the previous works. Natural images have approximately 30% improvement in coding efficiency and CG images have 60% improvement on average.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.113-121
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• 2013

In this paper, we propose a sub-pixel rendering algorithm using learning-based 2D FIR filters. The proposed algorithm consists of two stages: the learning and synthesis stages. At the learning stage, we produce the low-resolution synthesis information derived from a sufficient number of high/low resolution block pairs, and store the synthesis information into a so-called dictionary. At the synthesis stage, the best candidate block corresponding to each input high-resolution block is found in the dictionary. Next, we can finally obtain the low-resolution image by synthesizing the low-resolution block using the selected 2D FIR filter on a sub-pixel basis. On the other hand, we additionally enhance the sharpness of the output image by using pre-emphasis considering RGB stripe pattern of display. The simulation results show that the proposed algorithm can provide significantly sharper results than conventional down-sampling methods, without blur effects and aliasing.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.178-183
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• 2009

Online image dictionary has become more and more popular in concepts cognition. However, for existing online systems, only very few images are manually picked to demonstrate the concepts. Currently, there is very little research found on automatically choosing large scale online images with the help of semantic analysis. In this paper, we propose a novel framework to utilize community-generated online multimedia content to visually illustrate certain concepts. Our proposed framework adapts various techniques, including the correlation analysis, semantic and visual clustering to produce sets of high quality, precise, diverse and representative images to visually translate a given concept. To make the best use of our results, a user interface is deployed, which displays the representative images according the latent semantic coherence. The objective and subjective evaluations show the feasibility and effectiveness of our approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.77-85
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• 2013

This study aims to analyze the effects that video image in Seo-an's , claiming to China's first gigantic historic dance drama, has on the performance; it focuses on investigating which video image is used to accomplish the effects in showing specific themes and materials in . Image is meant by 'reflection of object', such as movie, television, dictionary, etc, with its coverage being extensive. The root of a word, image', is founded on imitary, signifying specifically and mentally visual representation. In other words, video image is considered combination of two synonymous words, 'video' and 'image'. Video is not just comprehension of traditional art genre, like literary value, theatrical qualities, and artistry of scenario, but wholeness as product, integrating original functions of all kinds of art and connecting subtle image creation of human being. The effects of video image represented in are as followings; first, expressive effect of the connotative meaning, reflecting the spirit of the age and its culture. Second, imaginary identification. Third, transformation scene. Fourth, dramatic interest through immersion. Last but not least, visual effect by dint of dimension of performance.

• Journal of the Korean Geographical Society
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• v.38 no.2
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• pp.275-291
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• 2003

It was essential to develop the DB-type teaching and teaming material for geography instruction using a method of ICT. The DB-type teaching and learning material was considered as a alternative in solving the problems of web-based geography instruction. Accordingly, in this study, the geography image DB program as developed, and based on this program the CD-ROM called GEO-DB, having the function of electronic dictionary of geography image for geography teaching and teaming was made. The GEO-DB was composed of 3,060 geography images collected by teachers and learners. The GEO-DB was made to be used simply by teachers and learners. Especially, the portfolio function was Included in the GEO-DB, and that was focused to the instructional system design of teacher and the self-directed teaming ability development of learner. Teachers and learners using this GEO-DB assessed that because the GEO-DB had the easiness of use, the speed of reference and the unlimitedness of extension, it could enlarge the possibility of using a method of In, and it could contribute to the development of geography teaming ability and the change of geography teaming attitude.