• ETRI Journal
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• v.33 no.5
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• pp.814-817
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• 2011

In this letter, the distributions of direct current (DC) coefficients for P-frames in H.264/AVC are analyzed, and the distortion model of the Gaussian source under the quantization of the dead-zone plus-uniform threshold quantization with uniform reconstruction quantizer is derived. Experimental results show that the DC coefficients of P-frames are best approximated by the Laplacian distribution and the Gaussian distribution at small quantization step sizes and at large quantization step sizes, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1512-1515
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• 1997

One of the advantages of master-slave teleoperation is scaling concept such as position scaling, force scaling Meanuhile, lots of quantization effects are generated from position and force sensors in the master and slave manipulator. In this paper, to show the output error caused by the quantizaion effects from the position sensor and position scaling factor, simulation is done for free motion without contact in slave side. Transfer functiion model in which the quantization effect is assumed to be a disturbance input to the system is derived. Model shows that Jacobian, scaling factors, and controller affect the output by quantization effects form esnsors. One dof master and slave are used for simulation. In our study, the higher sensor resolution decreases the output error form quantization. Scaling factors can amplify the quantizatiion effects form the sensors in master and slave manipulators.

• The Journal of the Acoustical Society of Korea
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• v.31 no.2
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• pp.93-99
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• 2012

For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.131-135
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• 1991

A quantization error model is suggested for analog to frequency(A/F) converter in strapdown inertial navigation system(SDINS),which is characterized by some white noise exciting the state variables. Also, effects on the performance of SDINS by analog to digital(A/D) converter and A/F converter are analyzed and compared via covariance simulation. As a result, A/F converter turns out to be superior to the A/D converter with respect to the induced navigation error and the difficulty in circuit realization. The quantization error model developed in this paper appears to be useful for optimal filter design.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.151-156
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• 2021

Low-end IoT devices do not have enough computation and memory resources for DNN learning and inference. Integer quantization of real-type neural network models can reduce model size, hardware computational burden, and power consumption. This paper describes the design and implementation of a web-based quantization platform for CNN deep learning accelerator chips. In the web service platform, we implemented visualization of the model through a convenient UI, analysis of each step of inference, and detailed editing of the model. Additionally, a data augmentation function and a management function of files that store models and inference intermediate results are provided. The implemented functions were verified using three YOLO models.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.9-17
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• 2023

Deep learning with large amount of computations is difficult to implement on micro-sized IoT devices or moblie devices. Recently, lightweight deep learning technologies have been introduced to make sure that deep learning can be implemented even on small devices by reducing the amount of computation of the model. Quantization is one of lightweight techniques that can be efficiently used to reduce the memory and size of the model by expressing parameter values with continuous distribution as discrete values of fixed bits. However, the accuracy of the model is reduced due to discrete value representation in quantization. In this paper, we introduce various quantization techniques to correct the accuracy. We selected APoT and EWGS from existing quantization techniques, and comparatively analyzed the results through experimentations The selected techniques were trained and tested with CIFAR-10 or CIFAR-100 datasets in the ResNet model. We found out problems with them through experimental results analysis and presented directions for future research.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1207-1214
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• 2014

The first frame of a GOP is encoded in intra mode which generates a larger number of bits. In addition, the first frame is used for the inter mode encoding of the following frames. Thus the encoding results of the intra frame affects the first frame as well as the following frames. Traditionally, the quantization parameter for an intra frame is determined only depending on the bpp not considering the characteristics of the intra frame. For accurate intra frame encoding, we should consider not only bpp but also the complexity of the video sequence and the output bandwidth. In this paper, we propose a real-time quantization model which is used to calculate the quantization parameter for an intra frame encoding based on the investigation on the characteristics of a GOP. It is shown by experimental results that the proposed quantization model captures the characteristics of an intra frame effectively and the proposed method for model parameters accurately estimates the real values.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.778-789
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• 2021

Recently, various lightweight methods for using Convolutional Neural Network(CNN) models in mobile devices have emerged. Weight quantization, which lowers bit precision of weights, is a lightweight method that enables a model to be used through integer calculation in a mobile environment where GPU acceleration is unable. Weight quantization has already been used in various models as a lightweight method to reduce computational complexity and model size with a small loss of accuracy. Considering the size of memory and computing speed as well as the storage size of the device and the limited network environment, this paper proposes a method of compressing integer weights after quantization using a video codec as a method. To verify the performance of the proposed method, experiments were conducted on VGG16, Resnet50, and Resnet18 models trained with ImageNet and Places365 datasets. As a result, loss of accuracy less than 2% and high compression efficiency were achieved in various models. In addition, as a result of comparison with similar compression methods, it was verified that the compression efficiency was more than doubled.

• ETRI Journal
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• v.29 no.1
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• pp.99-102
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• 2007

The channel level difference (CLD) is a main parameter in the reference model 0 (RM0) for MPEG Surround. Nevertheless, the CLD quantization method in the RM0 has problems such as the lack of theoretical background and inappropriate quantization levels. In this letter, a new CLD quantization method is proposed based on the virtual source location information which has strength in the quantization process. From experimental results, it is confirmed that the proposed scheme greatly reduces the quantization distortions measured in dB and degrees without any additional complexity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1364-1372
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• 2017

In this paper, a problem of event-triggered model predictive control is investigated for continuous-time Takagi-Sugeno (T-S) fuzzy systems with input quantization. To efficiently utilize network resources, event-trigger is employed, which transmits limited signals satisfying the condition that the measurement of errors is over the ratio of a certain level. Considering sampling and quantization, continuous Takagi-Sugeno (T-S) fuzzy systems are regarded as a sector bounded continuous-time T-S fuzzy systems with input delay. Then, a model predictive controller (MPC) based on parallel distributed compensation (PDC) is designed to optimally stabilize the closed loop systems. The proposed MPC optimize the objective function over infinite horizon, which can be easily calculated and implemented solving linear matrix inequalities (LMIs) for every event-triggered time. The validity and effectiveness are shown that the event triggered MPC can stabilize well the systems with even smaller average sampling rate and limited actuator signal guaranteeing optimal performances through the numerical example.