• Title/Summary/Keyword: vector quantization

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Envelope Elimination and Restoration Transmitter for Efficiency and Linearity Improvement of Power Amplifier (전력증폭기의 효율 및 선형성 개선을 위한 포락선 제거 및 복원 송신기)

  • Cho, Young-Kyun;Kim, Changwan;Park, Bong Hyuk
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.26 no.3
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    • pp.292-299
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    • 2015
  • An envelope elimination and restoration transmitter that uses a tri-level envelope encoding scheme is presented for improving the efficiency and linearity of the system. The proposed structure amplifies the same magnitude signal regardless of the input peak-to-average power ratio and reduces the quantization noise by spreading out the noise to the out-of-band frequency, resulting in the enhancement of power efficiency. An improved linearity is also obtained by providing a new timing mismatch calibration technique between the envelope and phase signal. Implementation in a 130 nm CMOS process, transmitter measurements on a 20-MHz long-term evolution input signal show an error vector magnitude of 3.7 % and an adjacent channel leakage ratio of 37.5 dBc at 2.13 GHz carrier frequency.

Integrating Color, Texture and Edge Features for Content-Based Image Retrieval (내용기반 이미지 검색을 위한 색상, 텍스쳐, 에지 기능의 통합)

  • Ma Ming;Park Dong-Won
    • Science of Emotion and Sensibility
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    • v.7 no.4
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    • pp.57-65
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    • 2004
  • In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

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A Study on Korean Phoneme Classification using Recursive Least-Square Algorithm (Recursive Least-Square 알고리즘을 이용한 한국어 음소분류에 관한 연구)

  • Kim, Hoe-Rin;Lee, Hwang-Su;Un, Jong-Gwan
    • The Journal of the Acoustical Society of Korea
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    • v.6 no.3
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    • pp.60-67
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    • 1987
  • In this paper, a phoneme classification method for Korean speech recognition has been proposed and its performance has been studied. The phoneme classification has been done based on the phonemic features extracted by the prewindowed recursive least-square (PRLS) algorithm that is a kind of adaptive filter algorithms. Applying the PRLS algorithm to input speech signal, precise detection of phoneme boundaries has been made, Reference patterns of Korean phonemes have been generated by the ordinery vector quantization (VQ) of feature vectors obtained manualy from prototype regions of each phoneme. In order to obtain the performance of the proposed phoneme classification method, the method has been tested using spoken names of seven Korean cities which have eleven different consonants and eight different vowels. In the speaker-dependent phoneme classification, the accuracy is about $85\%$ considering simple phonemic rules of Korean language, while the accuracy of the speaker-independent case is far less than that of the speaker-dependent case.

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HMM-based Speech Recognition using FSVQ, Fuzzy Concept and Doubly Spectral Feature (FSVQ, 퍼지 개념 및 이중 스펙트럼 특징을 이용한 HMM에 기초를 둔 음성 인식)

  • 정의봉
    • Journal of the Korea Computer Industry Society
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    • v.5 no.4
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    • pp.491-502
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    • 2004
  • In this paper, we propose a HMM model using FSVQ(First Section VQ), fuzzy theory and doubly spectral feature, as study on the isolated word recognition system of speaker-independent. In the proposed paper, LPC cepstrum coefficients and regression coefficients of LPC cepstrum as doubly spectral feature be used. And, training data are divided several section and first section is generated codebook of VQ, and then is obtained multi-observation sequences by order of large propabilistic values based on fuzzy nile from the codebook of the first section. Thereafter, this observation sequences of first section is trained and is recognized a word to be obtained highest probaility by same concept. Besides the speech recognition experiments of proposed method, we experiment the other methods under the equivalent environment of data and conditions. In the whole experiment, it is proved that the proposed method is superior to the others in recognition rate.

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The Variable Block-based Image Compression Technique using Wavelet Transform (웨이블릿 변환을 이용한 가변블록 기반 영상 압축)

  • 권세안;장우영;송광훈
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.7B
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    • pp.1378-1383
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    • 1999
  • In this paper, an effective variable-block-based image compression technique using wavelet transform is proposed. Since the statistical property of each wavelet subband is different, we apply the adaptive quantization to each wavelet subband. In the proposed algorithm, each subband is divided into non-overlapping variable-sized blocks based on directional properties. In addition, we remove wavelet coefficients which are below a certain threshold value for coding efficiency. To compress the transformed data, the proposed algorithm quantizes the wavelet coefficients using scalar quantizer in LL subband and vector quantizers for other subbands to increase compression ratio. The proposed algorithm shows improvements in compression ratio as well as PSNR compared with the existing block-based compression algorithms. In addition, it does not cause any blocking artifacts in very low bit rates even though it is also a block-based method. The proposed algorithm also has advantage in computational complexity over the existing wavelet-based compression algorithms since it is a block-based algorithm.

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Realtime No-Reference Quality-Assessment Over Packet Video Networks (패킷 비디오 네트워크상의 실시간 무기준법 동영상 화질 평가방법)

  • Sung, Duk-Gu;Kim, Yo-Han;Hana, Jung-Hyun;Shin, Ji-Tae
    • Journal of Broadcast Engineering
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    • v.14 no.4
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    • pp.387-396
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    • 2009
  • No-Reference video-quality assessments are divided into two kinds of metrics based on decoding pixel domain or the bitstream one. Traditional full-/reduced- reference methods have difficulty to be deployed as realtime video transmission because it has problems of additional data, complexity, and assessment accuracy. This paper presents simple and highly accurate no-reference video-quality assessment in realtime video transmission. Our proposed method uses quantization parameter, motion vector, and information of transmission error. To evaluate performance of the proposed algorithm, we perform subjective test of video quality with the ITU-T P.910 Absolute Category Rating(ACR) method and compare our proposed algorithm with the subjective quality assessment method. Experimental results show the proposed quality metric has a high correlation (85%) in terms of subjective quality assessment.

Texture Descriptor Using Correlation of Quantized Pixel Values on Intensity Range (화소값의 구간별 양자화 값 상관관계를 이용한 텍스춰 기술자)

  • Pok, Gouchol
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.3
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    • pp.229-234
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    • 2018
  • Texture is one of the most useful features in classifying and segmenting images. The LBP-based approach previously presented in the literature has been successful in many applications. However, it's theoretical foundation is based only on the difference of pixel values, and consequently it has a number of drawbacks like it performs poorly for the images corrupted with noise, and especially it cannot be used as a multiscale texture descriptor due to the exploding increase of feature vector dimension with increase of the number of neighbor pixels. In this paper, we present a method to address these drawbacks of LBP-based approach. More specifically, our approach quantizes the range of pixels values and construct a 3D histogram which captures the correlative information of pixels. This histogram is used as a texture feature. Several tests with texture images show that the proposed method outperforms the LBP-based approach in the problem of texture classification.

Relation Based Bayesian Network for NBNN

  • Sun, Mingyang;Lee, YoonSeok;Yoon, Sung-eui
    • Journal of Computing Science and Engineering
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    • v.9 no.4
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    • pp.204-213
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    • 2015
  • Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

Fast VQ Codebook Design by Sucessively Bisectioning of Principle Axis (주축의 연속적 분할을 통한 고속 벡터 양자화 코드북 설계)

  • Kang, Dae-Seong;Seo, Seok-Bae;Kim, Dai-Jin
    • Journal of KIISE:Software and Applications
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    • v.27 no.4
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    • pp.422-431
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    • 2000
  • This paper proposes a new codebook generation method, called a PCA-Based VQ, that incorporates the PCA (Principal Component Analysis) technique into VQ (Vector Quantization) codebook design. The PCA technique reduces the data dimensions by transforming input image vectors into the feature vectors. The cluster of feature vectors in the transformed domain is bisectioned into two subclusters by an optimally chosen partitioning hyperplane. We expedite the searching of the optimal partitioning hyperplane that is the most time consuming process by considering that (1) the optimal partitioning hyperplane is perpendicular to the first principal axis of the feature vectors, (2) it is located on the equilibrium point of the left and right cluster's distortions, and (3) the left and right cluster's distortions can be adjusted incrementally. This principal axis bisectioning is successively performed on the cluster whose difference of distortion between before and after bisection is the maximum among the existing clusters until the total distortion of clusters becomes as small as the desired level. Simulation results show that the proposed PCA-based VQ method is promising because its reconstruction performance is as good as that of the SOFM (Self-Organizing Feature Maps) method and its codebook generation is as fast as that of the K-means method.

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Recognition of License Plates Using a Hybrid Statistical Feature Model and Neural Networks (하이브리드 통계적 특징 모델과 신경망을 이용한 자동차 번호판 인식)

  • Lew, Sheen;Jeong, Byeong-Jun;Kang, Hyun-Chul
    • Journal of KIISE:Software and Applications
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    • v.36 no.12
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    • pp.1016-1023
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    • 2009
  • A license plate recognition system consists of image processing in which characters and features are extracted, and pattern recognition in which extracted characters are classified. Feature extraction plays an important role in not only the level of data reduction but also performance of recognition. Thus, in this paper, we focused on the recognition of numeral characters especially on the feature extraction of numeral characters which has much effect in the result of plate recognition. We suggest a hybrid statistical feature model which assures the best dispersion of input data by reassignment of clustering property of input data. And we verify the effectiveness of suggested model using multi-layer perceptron and learning vector quantization neural networks. The results show that the proposed feature extraction method preserves the information of a license plate well and also is robust and effective for even noisy and external environment.