• Title, Summary, Keyword: Quantization Model

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Selective Quantization Based on Band Property for Wideband Signal Codec (광대역 신호 압축기를 위한 주파수 대역 특성에 선택적인 양자화 방법)

  • 송재종;박호종;김무영;김도석;김정수
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.7
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    • pp.76-82
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    • 2001
  • In this paper, a novel quantization method for wideband signal codec with 7 kHz bandwidth is proposed. In the transform-based wideband signal codecs, the signal is transformed to frequency domain and the spectral coefficients in each frequency band are quantized based on human perceptual model, followed by Huffman coding. However, the property of each band varies with frequency, and the codec has poor performance when all bands are quantized with the same method. Therefore, a selective quantization method is proposed, which analyzes the band property and selects the quantization domain between frequency domain and time domain based on the quantization efficiency. It is confirmed that the proposed method has better performance than the quantizer of G722.1 codec.

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Vector Quantization based Speech Recognition Performance Improvement using Maximum Log Likelihood in Gaussian Distribution (가우시안 분포에서 Maximum Log Likelihood를 이용한 벡터 양자화 기반 음성 인식 성능 향상)

  • Chung, Kyungyong;Oh, SangYeob
    • Journal of Digital Convergence
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    • v.16 no.11
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    • pp.335-340
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    • 2018
  • Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

Adaptive Watermarking Using Successive Subband Quantization and Perceptual Model Based on Multiwavelet Transform Domain (멀티웨이브릿 변환 영역 기반의 연속 부대역 양자화 및 지각 모델을 이용한 적응 워터마킹)

  • 권기룡;이준재
    • Journal of Korea Multimedia Society
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    • v.6 no.7
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    • pp.1149-1158
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    • 2003
  • Content adaptive watermark embedding algorithm using a stochastic image model in the multiwavelet transform is proposed in this paper. A watermark is embedded into the perceptually significant coefficients (PSCs) of each subband using multiwavelet transform. The PSCs in high frequency subband are selected by SSQ, that is, by setting the thresholds as the one half of the largest coefficient in each subband. The perceptual model is applied with a stochastic approach based on noise visibility function (NVF) that has local image properties for watermark embedding. This model uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The watermark estimation use shape parameter and variance of subband region. it is derive content adaptive criteria according to edge and texture, and flat region. The experiment results of the proposed watermark embedding method based on multiwavelet transform techniques were found to be excellent invisibility and robustness.

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Vector Quantization by N-ary Search of a Codebook (코우드북의 절충탐색에 의한 벡터양자화)

  • Lee, Chang-Young
    • Speech Sciences
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    • v.8 no.3
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    • pp.143-148
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    • 2001
  • We propose a new scheme for VQ codebook search. The procedure is in between the binary-tree-search and full-search and thus might be called N-ary search of a codebook. Through the experiment performed on 7200 frames spoken by 25 speakers, we confirmed that the best codewords as good as by the full-search were obtained at moderate time consumption comparable to the binary-tree-search. In application to speech recognition by HMM/VQ with Bakis model, where appearance of a specific codeword is essential in the parameter training phase, the method proposed here is expected to provide an efficient training procedure.

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Physics-based Algorithm Implementation for Characterization of Gate-dielectric Engineered MOSFETs including Quantization Effects

  • Mangla, Tina;Sehgal, Amit;Saxena, Manoj;Haldar, Subhasis;Gupta, Mridula;Gupta, R.S.
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.5 no.3
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    • pp.159-167
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    • 2005
  • Quantization effects (QEs), which manifests when the device dimensions are comparable to the de Brogile wavelength, are becoming common physical phenomena in the present micro-/nanometer technology era. While most novel devices take advantage of QEs to achieve fast switching speed, miniature size and extremely small power consumption, the mainstream CMOS devices (with the exception of EEPROMs) are generally suffering in performance from these effects. In this paper, an analytical model accounting for the QEs and poly-depletion effects (PDEs) at the silicon (Si)/dielectric interface describing the capacitance-voltage (C-V) and current-voltage (I-V) characteristics of MOS devices with thin oxides is developed. It is also applicable to multi-layer gate-stack structures, since a general procedure is used for calculating the quantum inversion charge density. Using this inversion charge density, device characteristics are obtained. Also solutions for C-V can be quickly obtained without computational burden of solving over a physical grid. We conclude with comparison of the results obtained with our model and those obtained by self-consistent solution of the $Schr{\ddot{o}}dinger$ and Poisson equations and simulations reported previously in the literature. A good agreement was observed between them.

A Modified Gaussian Model-based Low Complexity Pre-processing Algorithm for H.264 Video Coding Standard (H.264 동영상 표준 부호화 방식을 위한 변형된 가우시안 모델 기반의 저 계산량 전처리 필터)

  • Song, Won-Seon;Hong, Min-Cheol
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.2C
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    • pp.41-48
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    • 2005
  • In this paper, we present a low complexity modified Gaussian model based pre-processing filter to improve the performance of H.264 compressed video. Video sequence captured by general imaging system represents the degraded version due to the additive noise which decreases coding efficiency and results in unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

Largest Coding Unit Level Rate Control Algorithm for Hierarchical Video Coding in HEVC

  • Yoon, Yeo-Jin;Kim, Hoon;Baek, Seung-Jin;Ko, Sung-Jea
    • IEIE Transactions on Smart Processing and Computing
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    • v.1 no.3
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    • pp.171-181
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    • 2012
  • In the new video coding standard, called high efficiency video coding (HEVC), the coding unit (CU) is adopted as a basic unit of a coded block structure. Therefore, the rate control (RC) methods of H.264/AVC, whose basic unit is a macroblock, cannot be applied directly to HEVC. This paper proposes the largest CU (LCU) level RC method for hierarchical video coding in a HEVC. In the proposed method, the effective bit allocation is performed first based on the hierarchical structure, and the quantization parameters (QP) are then determined using the Cauchy density based rate-quantization (RQ) model. A novel method based on the linear rate model is introduced to estimate the parameters of the Cauchy density based RQ model precisely. The experimental results show that the proposed RC method not only controls the bitrate accurately, but also generates a constant number of bits per second with less degradation of the decoded picture quality than with the fixed QP coding and latest RC method for HEVC.

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Compression of 3D Mesh Geometry and Vertex Attributes for Mobile Graphics

  • Lee, Jong-Seok;Choe, Sung-Yul;Lee, Seung-Yong
    • Journal of Computing Science and Engineering
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    • v.4 no.3
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    • pp.207-224
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    • 2010
  • This paper presents a compression scheme for mesh geometry, which is suitable for mobile graphics. The main focus is to enable real-time decoding of compressed vertex positions while providing reasonable compression ratios. Our scheme is based on local quantization of vertex positions with mesh partitioning. To prevent visual seams along the partitioning boundaries, we constrain the locally quantized cells of all mesh partitions to have the same size and aligned local axes. We propose a mesh partitioning algorithm to minimize the size of locally quantized cells, which relates to the distortion of a restored mesh. Vertex coordinates are stored in main memory and transmitted to graphics hardware for rendering in the quantized form, saving memory space and system bus bandwidth. Decoding operation is combined with model geometry transformation, and the only overhead to restore vertex positions is one matrix multiplication for each mesh partition. In our experiments, a 32-bit floating point vertex coordinate is quantized into an 8-bit integer, which is the smallest data size supported in a mobile graphics library. With this setting, the distortions of the restored meshes are comparable to 11-bit global quantization of vertex coordinates. We also apply the proposed approach to compression of vertex attributes, such as vertex normals and texture coordinates, and show that gains similar to vertex geometry can be obtained through local quantization with mesh partitioning.

Modeling Quantization Error using Laplacian Probability Density function (Laplacian 분포 함수를 이용한 양자화 잡음 모델링)

  • 최지은;이병욱
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.26 no.11A
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    • pp.1957-1962
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    • 2001
  • Image and video compression requires quantization error model of DCT coefficients for post processing, restoration or transcoding. Once DCT coefficients are quantized, it is impossible to recover the original distribution. We assume that the original probability density function (pdf) is the Laplacian function. We calculate the variance of the quantized variable, and estimate the variance of the DCT coefficients. We can confirm that the proposed method enhances the accuracy of the quantization error estimation.

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