• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1438-1440
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• 1987

In this paper, we discussed the quantization technique in the DCT domain employing a vector quantizer (VQ), and described the relations between the DCT and the VQ. And, we introduced a zonal coding technique for the OCT coefficients based on the classified VQ technique proposed in [2]. We shall show that this technique reduced the coding complexity about 30% while maintaining the same image quality as shown in [2]. A result of simulation with a natural image is also presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.34-38
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• 1996

In this paper, a new fast voronoi divider for vector quantization (VQ) is introduced, which results from Theorem that the nearest vectors in the sense of minimum mean square error(MMSE) have almost the same mean values of their elements. An improved splitting method for a VQ codebook design using the fast voronoi divider is also presented. Experimental results show that the new method reduces the complexity of training a VQ codebook several times with a high signal to noise ratio(SNR) using an appropriate extensive parameter of codebook.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.673-680
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• 2003

In this paper, we study the enhancement of VQ (Vector Quantization) design for text independent speaker recognition. In a concrete way, we present a non-iterative method which makes a vector quantization codebook and this method performs non-iterative learning so that the computational complexity is epochally reduced The proposed Classified Space VQ (CSVQ) design method for text Independent speaker recognition is generalized from Semi-noniterative VQ design method for text dependent speaker recognition. CSVQ contrasts with the existing desiEn method which uses the iterative learninE algorithm for every traininE speaker. The characteristics of a CSVQ design is as follows. First, the proposed method performs the non-iterative learning by using a Classified Space Codebook. Second, a quantization region of each speaker is equivalent for the quantization region of a Classified Space Codebook. And the quantization point of each speaker is the optimal point for the statistical distribution of each speaker in a quantization region of a Classified Space Codebook. Third, Classified Space Codebook (CSC) is constructed through Sample Vector Formation Method (CSVQ1, 2) and Hyper-Lattice Formation Method (CSVQ 3). In the numerical experiment, we use the 12th met-cepstrum feature vectors of 10 speakers and compare it with the existing method, changing the codebook size from 16 to 128 for each Classified Space Codebook. The recognition rate of the proposed method is 100% for CSVQ1, 2. It is equal to the recognition rate of the existing method. Therefore the proposed CSVQ design method is, reducing computational complexity and maintaining the recognition rate, new alternative proposal and CSVQ with CSC can be applied to a general purpose recognition.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.67-72
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• 2003

In this paper, we study the enhancement of VQ (Vector Quantization) design for text dependent speaker recognition. In a concrete way, we present the non-Iterative method which makes a vector quantization codebook and this method Is nut Iterative learning so that the computational complexity is epochally reduced. The proposed semi-noniterative VQ design method contrasts with the existing design method which uses the iterative learning algorithm for every training speaker. The characteristics of a semi-noniterative VQ design is as follows. First, the proposed method performs the iterative learning only for the reference speaker, but the existing method performs the iterative learning for every speaker. Second, the quantization region of the non-reference speaker is equivalent for a quantization region of the reference speaker. And the quantization point of the non-reference speaker is the optimal point for the statistical distribution of the non-reference speaker In the numerical experiment, we use the 12th met-cepstrum feature vectors of 20 speakers and compare it with the existing method, changing the codebook size from 2 to 32. The recognition rate of the proposed method is 100% for suitable codebook size and adequate training data. It is equal to the recognition rate of the existing method. Therefore the proposed semi-noniterative VQ design method is, reducing computational complexity and maintaining the recognition rate, new alternative proposal.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2254-2263
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• 2000

This paper proposes an efficient predictive image-compression technique based on vector quantization of blocks of pels. In the proposed method edge contents of blocks control the selection of predictors and block shapes as well. The maximum number of bits assigned to quantizers has been in creased to 3bits/pel from 1/5bits/pel, the setting employed by forerunners in predictive vector quantization of images. This increase prevents the saturation in SNR observed in their results in high bit rates. The variable block shape is instrumental in eh reconstruction of edges. The adaptive procedure is controlled by means of he standard deviation ofp rediction errors generated by a default predictor; the standard deviation address a decision table which can be set up beforehand. eh proposed method is characterized by overall improvements in image quality over A-VQ-PE and A-DCT VQ, both of which are known for their efficient use of vector quantizers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.81-90
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• 2005

When we transmit signals, which are quantized by the vector quantizer (VQ), through noisy channels, the overall performance of the coding system is very dependent on the employed quantization scheme and the channel error effect. In order to design an optimal coding system, the source and channel coding scheme should be jointly optimized as in the channel-optimized VQ. As a suboptimal approach, we may consider the robust VQ (RVQ). In RVQ, we consider developing an index assignment function for mapping the output of quantizers to channel symbols so that the effect of the channel errors is minimized. Recently, a VQ, which can reduce the encoding complexity and is called the sample-adaptive product quantizer (SAPQ), has been proposed. SAPQ has very similar quantizer structure as to the product quantizer (PQ). However, the quantization performance can be better than PQ. Further, the encoding complexity and the memory requirement for the codebooks are lower than the regular full-search VQ case. In this paper, SAPQ is employed in order to design an RVQ to channel errors by reducing the vector dimension. Discussions on the codebook structure of SAPQ and experiments are introduced in an aspect of robustness to noisy channels.

• Journal of Biomedical Engineering Research
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• v.10 no.2
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• pp.173-178
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• 1989

A vector quantization technique using a variable block size was applied to image compression of digitized X -ray films. Whether the size of VQ block should be subdivided or not is determined experimentally by the threshold value. The simulation result shows that the performance of the proposed vector quantizer is suitable for the medical image coding, which is applicable to PACS( Picture Archiving and Communication System).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.685-690
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• 1994

A problem associated with vector quantization(VQ) is the computational complexity incurred in searching for a codevector with the closet to a given input vector, where the complexity increases exponentionally with proportion to codebook size and then limits practical application. In this paper, a simple and fast, but efficient, VQ encoding algorithm is presented using a reference codevector as start codevector of premature exit condition, which eliminates distance claculation of unlikely codevectors. The algorithm is to find reference codevector having the possibility to be the nearest vector to input vector first and then to incorporate premature exit condition. The proposed algorithm needs only 10~15% of mathematical operations compared with the conventional full search VQ. Algorithm the number of additions and comparsions of the proposed algorithm is not reduced greatly, the number of multiplication is reduced up to 70~80% compared with other fast VQ encoding methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.2
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• pp.196-205
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• 1991

This paper is a study on the isolated word recognition of speaker independent which proposes to newly improved MSVQ(multisection vector quantization) recognition system which improve the classical MSVQ recognition system. It is a difference that test pattern has on more section than reference pattern in recognition system 146 DDD area names are selected as recognition vocabulary. 12th LPC cepstral coefficients is used as feature parameter. and when codebook is generated, MINSUM and MINMAX are used in finding the centroid. According to the experiment result. it is proved that this method is better than VQ(vector quantization) recognition methods, DTW(dynamic time warping) pattern matching methods and classical MSVQ methods for recognition rate and recognition time.