• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.28-35
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• 2010

The variable block size motion estimation (ME) and compensation (MC) using multiple reference frames is adopted in H.264/AVC to improve coding efficiency. However, the computational complexity for ME/MC increases proportional to the number of reference frames and variable blocks. In this paper, we propose a new efficient reference frame selection algorithm to reduce the complexity while keeping the visual quality. First, a motion vector reference map is constructed by SAD of $4{\times}4$ block unit for multi reference frames. Next, the variable block size motion estimation and motion compensation is performed according to the motion vector reference map. The computer simulation results show that the average loss of BDPSNR is -0.01dB, the increment of BDBR is 0.27%, and the encoding time is reduced by 38% compared with the original method for H.264/AVC.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.595-606
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• 2011

This paper proposes a robust watermarking scheme for GIS digital map by using the geometric properties of polyline and polygon, which are the fundamental components in vector data structure. In the proposed scheme, we calculate the lengths and the perimeters of all polylines and polygons in a map and cluster them to a number of groups. Then we embed the binary watermark by changing the mean of lengths and perimeters in an embedding group. For improving the safety and robustness, we permute the binary watermark through PRNS(pseudo-random number sequence) processing and embed it repeatedly in a model. Experimental results verified that our scheme has a good invisibility, safety and robustness to various geometric attacks and also our scheme needs not the original map in the extracting process of watermark.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.577-585
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• 2022

In this work, we introduced and study vector equilibrium problems for trifunction in measurable space (for short, VEPMS). The existence of solutions of (VEPMS) are obtained by employing Aumann theorem and Fan KKM lemma. As an application, we prove an existence result for vector variational inequality problem for measurable space. Our results in this paper are new which can be considered as significant extension of previously known results in the literature.

• Bulletin of the Korean Mathematical Society
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• v.38 no.4
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• pp.753-761
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• 2001

We introduce the notion of Gauss map of pointwise 1-type on ruled surfaces in the Euclidean 3-space for which vector valued functions is neither trivial nor it extends or coincides with the usual notion of 1-type, in general. We characterize the minimal helicoid in terms of it and give a complete classification of the ruled surfaces with pointwise 1-type Gauss map.

• Kyungpook Mathematical Journal
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• v.57 no.1
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• pp.133-144
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• 2017

In this paper, we study ruled surfaces in 3-dimensional Euclidean and Minkowski space in terms of their Gauss map. We obtain classification theorems for these type of surfaces whose Gauss map G satisfying ${\Box}G=f(G+C)$ for a constant vector $C{\in}{\mathbb{E}}^3$ and a smooth function f, where ${\Box}$ denotes the Cheng-Yau operator.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.48-56
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• 2009

We propose a fast intra mode skip decision algorithm for H.264/AVC video encoding. Although newly added MB encoding algorithms based on various prediction methods increase compression ratio, they require a significant increase in the computational complexity because we calculate rate-distortion(RD) cost for all possible MB coding modes and then choose the best one. In this paper, we propose a fast mode decision algorithm based on an adaptive motion vector map(AMVM) method for H.264/AVC video encoding to reduce the processing time for the inter frame. We verify that the proposed algorithm generates generally good performances in PSNR, bit rates, and processing time.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.81-91
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• 1994

A DCT map and new cldebook design algorithm based on a two-dimension discrete cosine transform (2D-DCT) is presented for coder of image vector quantizer. We divide the image into smaller subblocks, then, using 2D DCT, separate it into blocks which are hard to code but it bears most of the visual information and easy to code but little visual information, and DCT map is made. According to this map, the significant features of training image are extracted by using the 2D DCT. A codebook is generated by partitioning the training set into a binary tree based on tree-structure. Each training vector at a nonterminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. Compared with the pairwise neighbor (PPN) and classified VQ(CVQ) algorithm, about 'Lenna' and 'Boat' image, the new algorithm results in a reduction in computation time and shows better picture quality with 0.45 dB and 0.33dB differences as to PNN, 0.05dB and 0.1dB differences as to CVQ respectively.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.441-446
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• 2020

In this paper, we propose the α-Feature Map Scaling (α-FMS) method which extends the FMS method that was designed to enhance the discriminative power of feature maps of deep neural networks in Speaker Verification (SV) systems. The FMS derives a scale vector from a feature map and then adds or multiplies them to the features, or sequentially apply both operations. However, the FMS method not only uses an identical scale vector for both addition and multiplication, but also has a limitation that it can only add a value between zero and one in case of addition. In this study, to overcome these limitations, we propose α-FMS to add a trainable parameter α to the feature map element-wise, and then multiply a scale vector. We compare the performance of the two methods: the one where α is a scalar, and the other where it is a vector. Both α-FMS methods are applied after each residual block of the deep neural network. The proposed system using the α-FMS methods are trained using the RawNet2 and tested using the VoxCeleb1 evaluation set. The result demonstrates an equal error rate of 2.47 % and 2.31 % for the two α-FMS methods respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.57-65
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• 1995

This paper presents a new classified vector quantization (VQ) technique using a neural network model in the transform domain. Prior to designing a codebook, the proposed approach extracts class features from a set of images using self-organizing feature map (SOFM) that has the pattern recognition characteristics and the same as VQ objective. Since we extract the class features from the training images unlike previous approaches, the reconstructed image quality is improved. Moreover, exploiting the adaptivity of the neural network model makes our approach be easily applied to designing a new vector quantizer when the processed image characteristics are changed. After the generalized BFOS algorithm allocates the given bits to each class, codebooks of each class are also generated using SOFM for the maximal reconstructed image quality. In experimental results using monochromatic images, we obtained a good visual quality in the reconstructed image. Also, PSNR is comparable to that of other classified VQ technique and is higher than that of JPEG baseline system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.581-590
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• 2014

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.