• The Journal of the Acoustical Society of Korea
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• v.12 no.1
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• pp.55-60
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• 1993

본 논문은 불특정 화자의 단모음 인식에 관한 연구로써, VQ(Vectro Quantization)와 MLP(multi-layer perceptron)에 의한 음성 인식 방법을 제안한다. 이 방법은 VQ codebook을 구하고 이를 이용해서 관측열(observation sequence)을 구해각 codeword가 데이터로부터 가질 수 있는 확률값을 계산하여 이 값을 신경 회로망의 입력으로 사용하는 방법이다. 인식 대상으로는 한국어 단모음을 선정하였으며 10명의 남성 화자가 8개의 단모음을 10번씩 발음한 것으로 시스템의 효율성을 알아보기 위해 VQ/HMM(hidden markov model)에 의한 인식과 비교 실험한다. 실험 결과에 의하면, 시스템의 단순성에도 불구하고 학습능력애 뛰어난 관계로 VQ/HMM보다 VQ와 MLP에 의한 음성 인식률이 향상됨을 보여준다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.302-308
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• 1988

The paper describes a very simple algorithm for reducing the encoding complexity of vector quantization(VQ), exploiting the feature of a vector currently being encoded. A proposed VQ of 16(=4x4) vector dimension shows a slight performance degradation of about 0.1-1.9dB, however, with only 16-32 among 256 codeword searches, i.e., with just 1/16-1/8 search complexity compared to a full-search VQ. And the proposed VQ scheme is also compared to outperform tree-search VQ with regard to their SNR performance and memory requirement.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.35-38
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• 1987

The purpose of this paper is to compare a class of vector quantization techniques which include GVQ(Genera VQ) MSVQ(Mean separated VQ) and DCT_VQ The VQ techniques are applied to six images and both subjective and objective performance comparison are made The results indicate that the transform domain approach(DCT_VQ) yields more syable results than the spatial domain method (GVQ, MSVQ)

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2913-2929
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• 2014

Traditional vector quantization (VQ) schemes encode image blocks as VQ indices, in which there is significant similarity between the image block and the codeword of the VQ index. Thus, the method can compress an image and maintain good image quality. This paper proposes a novel lossless VQ indices compression algorithm to further compress the VQ index table. Our scheme exploits the high correlation of adjacent image blocks to search for the same VQ index with the current encoding index from the neighboring indices. To increase compression efficiency, codewords in the codebook are sorted according to the degree of similarity of adjacent VQ indices to generate a state codebook to find the same index with the current encoding index. Note that the repetition indices both on the search path and in the state codebooks are excluded to increase the possibility for matching the current encoding index. Experimental results illustrated the superiority of our scheme over other compression schemes in the index domain.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.365-371
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• 2014

In the era of big data, algorithms for the existing IT environment cannot accept on a distributed architecture such as hadoop. Thus, new distributed algorithms which apply a distributed framework such as MapReduce are needed. Lloyd's algorithm commonly used for vector quantization is developed using MapReduce recently. In this paper, we proposed a decombined distributed VQ codebook generation algorithm based on a distributed VQ codebook generation algorithm using MapReduce to get a result more fast. The result of applying the proposed algorithm to big data showed higher performance than the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6C
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• pp.580-593
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• 2003

A joint design scheme has been proposed to optimize the source encoder and the modulation signal constellation based on the minimization of the end-to-end distortion including both the quantization error and channel distortion. The proposed scheme first optimizes the VQ codebook for a fixed modulation signal set, and then the modulation signals for the fixed VQ codebook. These two steps are iteratively repeated until they reach a local optimum solution. It has been shown that the performance of the proposed system can be enhanced by employing a new efficient mapping scheme between codevectors and modulation signals. Simulation results show that a jointly optimized system based on the proposed algorithms outperforms the conventional system based on a conventional QAM modulation signal set and the VQ codebook designed for a noiseless channel.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.87-94
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• 2007

In this paper, the performance of the speech recognition and speaker adaptation methods are studied. The speech recognition using energy state and VQ(Vector Quantization) is suggested and the speaker adaptation methods(Maximum a posteriori probability estimation, linear specrum estimation) are considered. The experimental results show that recognition ration using energy state is 2-3 % better than that of general VQ.

• Speech Sciences
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• v.7 no.2
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• pp.235-244
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• 2000

This paper was focused on realizing the text-independent speaker recognizer using the VQ and GMM algorithm and studying the characteristics of the speaker recognizers that adopt these two algorithms. Because it was difficult ascertain the effect two algorithms have on the speaker recognizer theoretically, we performed the recognition experiments using various parameters and, as the result of the experiments, we could show that GMM algorithm had better recognition performance than VQ algorithm as following. The GMM showed better performance with small training data, and it also showed just a little difference of recognition rate as the kind of feature vectors and the length of input data vary. The GMM showed good recognition performance than the VQ on the whole.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1091-1098
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• 1995

This paper proposes an efficient noiseless encoding method of vector quantization(VQ) index using a dynamic index mapping. Using high interblock correlation, the proposed index mapper transforms an index into a new one with lower entropy. In order to achieve good performance with low computational complexity, we adopt 'the sum of differences in pixel values on the block boundaries' as the cost function for index mapping. Simulation results show that the proposed scheme reduces the average bitrate by 40 - 50 % in ordinary VQ system for image compression. In addition, it is shown that the proposed index mapping method can be also applied to mean-residual VQ system, which allows the reduction of bitrate for VQ index by 20 - 30 %(10 - 20 % reduction in total bitrate). Since the proposed scheme is one for noiseless encoding of VQ index, it provides the same quality of the reconstructed image as the conventional VQ system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2984-3004
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• 2015

Designing a new reversible data hiding technique with a high embedding rate and a low compression rate for vector quantization (VQ) compressed images is encouraged. This paper proposes a novel lossless data hiding scheme for VQ-compressed images based on the joint neighboring coding technique. The proposed method uses the difference values between a current VQ index and its left and upper neighboring VQ indexes to embed n secret bits into one VQ index, where n = 1, 2, 3, or 4. The experimental results show that the proposed scheme achieves the embedding rates of 1, 2, 3, and 4 bits per index (bpi) with the corresponding average compression rates of 0.420, 0.483, 0.545, and 0.608 bit per pixel (bpp) for a 256 sized codebook. These results confirm that our scheme performs better than other selected reversible data hiding schemes.