• 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 the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.19-30
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• 2012

For digital data compression, the quantization is the main part of the lossy source coding. In order to improve the performance of quantization, the vector quantizer(VQ) can be employed. The encoding complexity, however, exponentially increases as the vector dimension or bit rate gets large. Much research has been conducted to alleviate such problems of VQ. Especially for high bit rates, a constrained VQ, which is called the sample-adaptive product quantizer(SAPQ), has been proposed for reducing the hugh encoding complexity of regular VQs. SAPQ has very similar structure as to the product VQ(PQ). However, the quantizer performance can be better than the PQ case. Further, the encoding complexity and the memory requirement for the codebooks are lower than the regular full-search VQ case. Among SAPQs, 1-SAPQ has a simple quantizer structure, where each product codebook is symmetric with respect to the diagonal line in the underlying vector space. It is known that 1-SAPQ shows a good performance for i.i.d. sources. In this paper, a study on designing 1-SAPQ for the first-order Markov process. For an efficient design of 1-SAPQ, an algorithm for the initial codebook is proposed, and through the numerical analysis it is shown that 1-SAPQ shows better quantizer distortion than the VQ case, of which encoding complexity is similar to that of 1-SAPQ, and shows distortions, which are close to that of the DPCM(differential pulse coded modulation) scheme with the Lloyd-Max quantizer.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.75-82
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• 1998

Conventional vector quantization (VQ) codebook design methods have several drawbacks such as edge degradation and high computational complexity. In this paper, we first made activity coordinates from the horizonatal and the vertical activity of the input block. Then it is mapped on the 2-dimensional interconnected codebook, and the codebook is designed using kohonen self-organizing map (KSFM) learning algorithm after the search of a codevector that has the minumum distance from the input vector in a small window, centered by the mapped point. As the serch area is restricted within the window, the computational amount is reduced compared with usual VQ. From the resutls of computer simulation, proposed method shows a better perfomance, in the view point of edge reconstruction and PSNR, than previous codebook training methods. And we also obtained a higher PSNR than that of classified vector quantization (CVQ).

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.5-11
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• 1996

This paper is the study for recognizing single vowels of speaker-independent, and we suppose a method of speech recognition using VQ(Vector Quantization)/NN(Neural Network). This method makes a VQ codebook, which is used for obtaining the observation sequence, and then claculates the probability value by comparing each codeword with the data, finally uses these probability values for the input value of the neural network. Korean signle vowels are selected for our recognition experiment, and ten male speakers pronounced eight single vowels ten times. We compare the performance of our method with those of fuzzy VQ/HMM and conventional VQ/NN According to the experiment result, the recognition rate by VQ/NN is 92.3%, by VQ/HMM using fuzzy is 93.8% and by VQ/NN using fuzzy is 95.7%. Therefore, it is shown that recognition rate of speech recognition by fuzzy VQ/NN is better than those of fuzzy VQ/HMM and conventional VQ/HMM because of its excellent learning ability.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.285-288
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• 2002

Compression of magnetic resonance images (MRI) has proved to be more difficult than other medical imaging modalities. In an average sized hospital, many tora bytes of digital imaging data (MRI) are generated every year, almost all of which has to be kept. The medical image compression is currently being performed by using different algorithms. In this paper, Fuzzy C-Means (FCM) algorithm is used for the Vector Quantization (VQ). First, a digital image is divided into subblocks of fixed size, which consists of 4${\times}$4 blocks of pixels. By performing 2-D Discrete Cosine Transform (DCT), we select six DCT coefficients to form the feature vector. And using FCM algorithm in constructing the VQ codebook. By doing so, the algorithm can make good time quality, and reduce the processing time while constructing the VQ codebook.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.37-44
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• 2009

Digital watermarking is a technology for preventing illegal copying, for protecting intellectual property rights and copyrights, and for suggesting grounds of the ownership by inserting watermarks into digital contents. Generally speaking, watermarking techniques cannot escape from data distortion and quality degradation due to the watermark insertion. In order to overcome the shortcoming, zero-watermarking techniques which do not change the original data have been proposed recently. This paper proposes CSMVQ(Chaotic SMVQ), a zero-watermarking system for SMVQ(Side Match Vector Quantization) which shows better compression ratio and quality and less blocking effect than VQ(Vector Quantization). In SMVQ, compression progresses from left top to right bottom in order to use the information of the two neighbor blocks, so it is impossible to insert watermarks chaotically. In the process of encoding, CSMVQ dynamically considers the information of the (1 to 4) neighbor blocks already encoded. Therefore, watermark can be inserted into digital contents in chaotic way. Experimental results show that the image quality compressed by CSMVQ is better than that of SMVQ and the inserted watermark is robust against some common attacks.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4E
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• pp.31-38
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• 2001

A large-scale synthesis database for a unit selection based synthesis method usually retains redundant synthesis unit instances, which are useless to the synthetic speech quality. In this paper, to eliminate those instances from the synthesis database, we proposed a new pruning method called weighted vector quantization (WVQ). The WVQ reflects relative importance of each synthesis unit instance when clustering the similar instances using vector quantization (VQ) technique. The proposed method was compared with two conventional pruning methods through the objective and subjective evaluations of the synthetic speech quality: one to simply limit maximum number of instance, and the other based on normal VQ-based clustering. The proposed method showed the best performance under 50% reduction rates. Over 50% of reduction rates, the synthetic speech quality is not seriously but perceptibly degraded. Using the proposed method, the synthesis database can be efficiently reduced without serious degradation of the synthetic speech quality.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.87-90
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• 2008

디지털 워터마킹은 디지털 콘텐츠에 워터마크를 삽입함으로써 불법적인 복제를 방지하고, 지적재산권 및 저작권을 보호하며, 소유권을 주장할 수 있는 근거를 제시하는 기술이다. 기존의 워터마킹 기법에서는 워터마크를 삽입함으로써 데이터 왜곡과 품질 저하가 불가피하다는 단점이 있다. 이를 극복하기 위하여 원래 데이터를 변경하지 않는 제로-워터마킹 기법들이 제시되고 있다. 이 논문에서는 VQ(Vector Quantization) 방식의 블록 효과를 줄이고, 압축 비율과 품질을 향상시킨 SMVQ(Side Match Vector quantization) 방식에 대한 제로-워터마킹 체계인 CSMVQ(Chaotic SMVQ)를 제안한다. SMVQ 이미지 압축에서는 동일하게 두 이웃 블록의 접면 정보를 이용하기 위하여 좌측 상단에서 우측 하단으로 진행되므로, 임의의 순서로 블록을 선택하여 워터마크를 삽입하기 어렵다. CSMVQ에서는 이전에 부호화된 이웃 블록들의 접면 정보의 일치성을 동적으로 고려하여 부호화를 진행한다. 그러므로 무질서한 방식으로 워터마크가 삽입되도록 지원할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5170-5188
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• 2015

This paper proposes a novel progressive secret image-hiding scheme based on the inpainting technique, the vector quantization technique (VQ) and the exploiting modification direction (EMD) technique. The proposed scheme first divides the secret image into non-overlapping blocks and categorizes the blocks into two groups: complex and smooth. The blocks in the complex group are compressed by VQ with PCA sorted codebook to obtain the VQ index table. Instead of embedding the original secret image, the proposed method progressively embeds the VQ index table into the cover images by using the EMD technique. After the receiver recovers the complex parts of the secret image by decoding the VQ index table from the shadow images, the smooth parts can be reconstructed by using the inpainting technique based on the content of the complex parts. The experimental results demonstrate that the proposed scheme not only has the advantage of progressive data hiding, which involves more shadow images joining to recover the secret image so as to produce a higher quality steganography image, but also can achieve high hiding capacity with acceptable recovered image quality.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.359-361
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• 1999

내용 기반 영상 검색을 위해 본 연구에서는 Vector Quantization을 이용하여 영상을 구성하는 주요 객체들의 특징 추출 방법을 제안한다. 내용 기반 검색 시스템에서 사용되는 영상의 주요 특징들은 색상, 질감, 형태 및 영상을 구성하고 있는 객체들의 공간적 위치 등이 사용된다. 이러한 특징들 중에서 어떤 특징들을 사용하고 또 어떤 방식으로 결합하느냐에 따라 혹은 영상의 특성을 잘 나타낼 수 있는 주요 특징을 어떻게 추출, 표현하느냐에 따라 검색 성능에 큰 영향을 미친다. 이 중 본 논문에서는 일반적인 색상, 질감 특징 추출방법과 더불어 Vector Quantization 알고리즘을 이용하여 정지 영상을 구성하고 있는 객체들의 대표 색상과 질감 특징을 빠르게 추출하고 이를 내용 기반 검색에 이용함으로써 객체의 위치, 회전 및 크기 변화에 무관한 검색을 가능케 했다. 연구의 실험 결과 VQ를 이용함으로써 대표특징치 추출시간을 줄일 수 있었고 검색시 색상과 질감 특징의 가중치를 각각 0.5, 0.5로 주는 것이 가장 높은 검출율을 보였으며 제안된 방식에 의해 '사람' 영상의 경우 0.9의 검출율을 보였다.