• Title, Summary, Keyword: Quantization Model

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An Optimal Selection of Frame Skip and Spatial Quantization for Low Bit Rate Video Coding (저속 영상부호화를 위한 최적 프레임 율과 공간 양자화 결정)

  • Bu, So-Young;Lee, Byung-Uk
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.6C
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    • pp.842-847
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    • 2004
  • We present a new video coding technique to tradeoff frame rate and picture quality for low bit rate video coding. We show a model equation for selecting the optimal frame rate from the motion content of the source video. We can determine DCT quantization parameter (QP) using the frame rate and bit rate. For objective video quality measurement we propose a simple and effective error measure for skipped frames. The proposed method enhances the video quality up to 2 ㏈ over the H.263 TMN5 encoder.

Pattern Classification Model using LVQ Optimized by Fuzzy Membership Function (퍼지 멤버쉽 함수로 최적화된 LVQ를 이용한 패턴 분류 모델)

  • Kim, Do-Tlyeon;Kang, Min-Kyeong;Cha, Eui-Young
    • Journal of KIISE:Software and Applications
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    • v.29 no.8
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    • pp.573-583
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    • 2002
  • Pattern recognition process is made up of the feature extraction in the pre-processing, the pattern clustering by training and the recognition process. This paper presents the F-LVQ (Fuzzy Learning Vector Quantization) pattern classification model which is optimized by the fuzzy membership function for the OCR(Optical Character Recognition) system. We trained 220 numeric patterns of 22 Hangul and English fonts and tested 4840 patterns whose forms are changed variously. As a result of this experiment, it is proved that the proposed model is more effective and robust than other typical LVQ models.

An Adaptive Rate Control Using Piecewise Linear Approximation Model (부분 선형 근사 모델을 이용한 적응적 비트율 제어)

  • 조창형;정제창;최병욱
    • Journal of Broadcast Engineering
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    • v.2 no.2
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    • pp.194-205
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    • 1997
  • In video compression standards such as MPEG and H.263. rate control is one of the key components for good coding performance. This paper presents a simple adaptive rate control scheme using a piecewise linear approximation model. While conventional buffer control approach is performed by adjusting the quantization parameter linearly according to the buffer fullness. the proposed approach uses a piecewise linear approximation model derived from logarithmic relation between the quantization parameter and bitrate in data compression. In addition. a forward analyzer performed in the spatial domain is used to improve image quality. Simulation results demonstrate that the proposed method provides better performance than the conventional one and reduces the fluctuation of the PSNR per frame while maintaining the quality of the reconstructed frames at a relatively stable level.

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Sequential Speaker Classification Using Quantized Generic Speaker Models (양자화 된 범용 화자모델을 이용한 연속적 화자분류)

  • Kwon, Soon-Il
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.44 no.1
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    • pp.26-32
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    • 2007
  • In sequential speaker classification, the lack of prior information about the speakers poses a challenge for model initialization. To address the challenge, a predetermined generic model set, called Sample Speaker Models, was previously proposed. This approach can be useful for accurate speaker modeling without requiring initial speaker data. However, an optimal method for sampling the models from a generic model pool is still required. To solve this problem, the Speaker Quantization method, motivated by vector quantization, is proposed. Experimental results showed that the new approach outperformed the random sampling approach with 25% relative improvement in error rate on switchboard telephone conversations.

A Simple Transcoding Method for H.264 Coding System (H.264 부호화시스템에서 간단한 비트열 변환 기법)

  • Yang, Young-Hyun;Kwon, Soon-Kak
    • Journal of Korea Multimedia Society
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    • v.9 no.7
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    • pp.818-826
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    • 2006
  • In this paper, we investigate the relationship of bitrate and quantization parameter needed for the trans-coding method that makes the H.264 bitstream of a particular bitrate to the other titrate. Also we propose the new method in order to transcode the titrate between H.264 video coded bitstreams. The proposed transcoding method updates the model parameters from previous picture or slice by using the approximated relationship of bitrate and quantization step-size and finds the target quantization step-size, and then generates the target titrate by simple coding processing just after requantization. Therefore, the proposed method does not need the complex bitrate control and converts to the target titrate by simple implementation. From simulation, we can see that the proposed method transcodes exactly to an assigned target bitrate for the four test sequences with different their characteristics.

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Fixed-point Implementation for Downlink Traffic Channel of IEEE 802.16e OFDMA TDD System (IEEE 802.16e OFDMA TDD 시스템 하향링크 트래픽 채널의 Fixed-point 구현 방법론)

  • Kim Kyoo-Hyun;Sun Tae-Hyung;Wang Yu-Peng;Chang Kyung-Hi;Park Hyung-Il;Eo Ik-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.6A
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    • pp.593-602
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    • 2006
  • This paper propose to methodology for deciding suitable bit size that minimizes hardware complexity and performance degradation from floating-point design the fixed-point implementation of downlink traffic channel of IEEE 802.16e OFDMA TDD system. One of the major considering issues for implementing fixed-point design is to select Saturation or Quantization properly with the knowledge of signal distribution by pdf or histogram. Also, through trial and error, we should execute exhaustive computer simulation for various bit sizes, hence obtain appropriate bit size while minimizing performance degradation. We carry out computer simulation to decide the optimized bit size of downlink traffic channel under AWGN and ITU-R M.1225 Veh-A channel model.

Face Recognition using Vector Quantizer in Eigenspace (아이겐공간에서 벡터 양자기를 이용한 얼굴인식)

  • 임동철;이행세;최태영
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.5
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    • pp.185-192
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    • 2004
  • This paper presents face recognition using vector quantization in the eigenspace of the faces. The existing eigenface method is not enough for representing the variations of faces. For making up for its defects, the proposed method use a clustering of feature vectors by vector quantization in eigenspace of the faces. In the trainning stage, the face images are transformed the points in the eigenspace by eigeface(eigenvetor) and we represent a set of points for each people as the centroids of vector quantizer. In the recognition stage, the vector quantizer finds the centroid having the minimum quantization error between feature vector of input image and centriods of database. The experiments are performed by 600 faces in Faces94 database. The existing eigenface method has minimum 64 miss-recognition and the proposed method has minimum 20 miss-recognition when we use 4 codevectors. In conclusion, the proposed method is a effective method that improves recognition rate through overcoming the variation of faces.

HMM-based Speech Recognition using FSVQ and Fuzzy Concept (FSVQ와 퍼지 개념을 이용한 HMM에 기초를 둔 음성 인식)

  • 안태옥
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.40 no.6
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    • pp.90-97
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    • 2003
  • This paper proposes a speech recognition based on HMM(Hidden Markov Model) using FSVQ(First Section Vector Quantization) and fuzzy concept. In the proposed paper, we generate codebook of First Section, and then obtain multi-observation sequences by order of large propabilistic values based on fuzzy rule from the codebook of the first section. Thereafter, this observation sequences of first section from codebooks is trained and in case of recognition, a word that has the most highest probability of first section is selected as a recognized word by same concept. Train station names are selected as the target recognition vocabulary and LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments of proposed method, we experiment the other methods under same conditions and data. Through the experiment results, it is proved that the proposed method based on HMM using FSVQ and fuzzy concept is superior to tile others in recognition rate.

Adaptive Model-Based Quantization Parameter Decision for Video Rate Control (비디오 비트율 제어를 위한 적응적 모델 기반의 양자화 변수 결정 방법)

  • Kim, Seon-Ki;Ho, Yo-Sung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.4C
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    • pp.411-417
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    • 2007
  • The rate control is an essential component in video coding to provide better quality under given coding constraints, such as channel capacity, frame rates, etc. In general, source data cannot be described as a single distribution in a video coding, hence it can cause an exhaustive approximation problem. It drops a coding efficiency under weak channel environments, such as mobile communications. In this paper, we design a new quantization parameter decision model that is based on a rate-distortion function of generalized Gaussian distribution. In order to adaptively express various source data distribution, we decide a shape parameter by observing a ratio of samples, which have a small value. For experiment, the proposed algorithm is implemented into H.264/AVC video codec, and its performance is compared with that of MPEG-2 TM5, H.263 TMN8 rate control algorithm. As shown in simulation results, the proposed algorithm provides an improved quality rather than previous algorithms and generates the number of bits closed to the target bits.

Sensitivity Property of Generalized CMAC Neural Network

  • Kim, Dong-Hyawn;Lee, In-Won
    • Computational Structural Engineering : An International Journal
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    • v.3 no.1
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    • pp.39-47
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    • 2003
  • Generalized CMAC (GCMAC) is a type of neural network known to be fast in learning. The network may be useful in structural engineering applications such as the identification and the control of structures. The derivatives of a trained GCMAC is relatively poor in accuracy. Therefore to improve the accuracy, a new algorithm is proposed. If GCMAC is directly differentiated, the accuracy of the derivative is not satisfactory. This is due to the quantization of input space and the shape of basis function used. Using the periodicity of the predicted output by GCMAC, the derivative can be improved to the extent of having almost no error. Numerical examples are considered to show the accuracy of the proposed algorithm.

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