• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.529-535
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• 2000

In this paper, we propose a new method of designing the vector quantizer which is robustness to coding results and independent of statistical characteristics of an input image in wavelet transformed image coding processes. The most critical drawback of a conventional vector quantizer is the degradation of coding capability resulted from the discordance between quantizer objective image and statistical characteristics of training sequence which is for generating representing vector. In order to resolve the problem of conventional methods, we use independent random-variables and pseudo image to which image correlation and edge component were added, as a training sequence for generating representing vector. We have done a computer simulation in order to compare coding capability between a vector quantizer designed by the proposed method and one with the conventional method using real image as same as that is objective to coding of training sequence used in codebook generation. The results show the superiority of the proposed vector quantizer method at the aspect of coding capability compared to conventional one. They also clarify the problems of conventional methods.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.95-100
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• 1996

In this paper, we present an image coder using variable quantizer for newly proposed JPEG extensions which has been standardized as ISO/IEC 10918-3(ITU-T Rec. T.84). It is necessary to alleviate the blocking artifact which is more sensitive to human eye in view of the spatial frequency sensitivity. The blocking artifact arises in the lower activity area rather than in the higher area. Therefore variable quantizer use the horizontal and vertical derivatives for calculating the $8{\times}8$ block activity. We classified nonlinear quantizer parameter into 5 categories in order to finely quantize in the lower active region. As a result of simulation for various images, the proposed coder increases subjective and objective quality at a given bit rate.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.67-75
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• 2012

Optimal quantizers in conducting the entropy-constrained quantization for high bit rates have the lattice structure. The quantization process is simple due to the regular structure, and various quantization algorithms are proposed depending on the lattice. Such a lattice vector quantizer (VQ) can be implemented by using the sample-adaptive product quantizer (SAPQ) and its output can also be easily entropy encoded. In this paper, the entropy encoding scheme for the lattice VQ is proposed based on SAPQ, and the performance of the proposed lattice VQ, which is based on SAPQ with the entropy coder, is asymptotically compared as the rate increases. It is shown by experiment that the gain for the memoryless Gaussian source also approaches the theoretic gain for the uniform density case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2391-2400
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• 1999

Vector quantizer (VQ) is an efficient data compression technique for low bit rate applications. However, the major disadvantage of VQ is its encoding complexity which increases dramatically as the vector dimension and bit rate increase. Even though one can use a modified VQ to reduce the encoding complexity, it is nearly impossible to implement such a VQ at a high bit rate or for a large vector dimension because of the enormously large memory requirement for the codebook and the very large training sequence (TS) size. To overcome this difficulty, in this paper we propose a novel structurally constrained VQ for the high bit rate and the large vector dimension cases in order to obtain VQ-level performance. Furthermore, this VQ can be extended to the low bit rate applications. The proposed quantization scheme has a form of feed-forward adaptive quantizer with a short adaptation period. Hence, we call this quantization scheme sample-adaptive product quantizer (SAPQ). SAPQ can provide a 2 ~3dB improvement over the Lloyd-Max scalar quantizers.

• Convergence Security Journal
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• v.6 no.4
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• pp.83-90
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• 2006

In this paper, we propose a new design method for a robust vector quantizer that is independent of the statistical characteristics of input images in the wavelet transformed image coding. The conventional vector quantizers have failed to get quality coding results because of the different statistical properties between the image to be quantized and the training sequence for a codebook of the vector quantizer. Therefore, in order to solve this problem, we used a pseudo image as a training sequence to generate a codebook of the vector quantizer; the pseudo image is created by adding correlation coefficient and edge components to uniformly distributed random numbers. We will clearly define the problem of the conventional vector quantizers, which use real images as a training sequence to generate a codebook used, by comparing the conventional methods with the proposed through computer simulation. Also, we will show the proposed vector quantizer yields better coding results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.57-62
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• 2019

This paper evaluates the QE-MMA (Quantized Error-MMA) adaptive equalization algorithm by the number of quantizer in order to compensates the intersymbol interference due to channel in the transmission of high spectral efficient nonconstant modulus signal. In the adaptive equalizer, the error signal is needed for the updating the tap coefficient, the QE-MMA uses the polarity of error signal and correlation multiplier that condered nonlinear finite bit power-of-two quantizing component in order to convinience of H/W implementation. The different adaptive equalization performance were obtained by the number of quantizer, these performance were evaluated by the computer simulation. For this, the equalizer output signal constellation, residual isi, maximum distortion, MSE, SER were applied as a performance index. As a result of computer simulation, it improved equalization performance and reduced equalization noise were obtained in the steady state by using large quantizer bit numbers, but gives slow in convergence speed for reaching steady state.

• ETRI Journal
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• v.23 no.1
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• pp.16-22
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• 2001

Efficient quantization methods of the line spectrum pairs (LSP) which have good performances, low complexity and memory are proposed. The adaptive quantization range method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector-scalar hybrid quantizer. As the maximum quantization range of each LSP parameter is varied adaptively on the quantized value of the previous order's LSP parameter, efficient quantization methods can be obtained. The proposed scalar quantization algorithm needs 31 bits/frame, which is 3 bits less per frame than in the conventional scalar quantization method with interframe prediction to maintain the transparent quality of speech. The improved vector-scalar quantizer achieves an average spectral distortion of 1 dB using 26 bits/frame. The performances of proposed quantization methods are also evaluated in the transmission errors.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.33-38
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• 1996

This paper provides an approach for representing an optimum vector quantizer by a scalar nonlinear gain-plus-additive noise model. The validity and accuracy of this analytic model is confirmed by comparing the calcuated model quantization errors with actual simulation of the optimum Linde-Buzo-Gray(LBG) vector quantizer. Using this model we frm MSE measure of an M-band filter bank codec in terms of the equivalent scalar quantizatin model and find the optimum FIR filter coefficients for each channel in the M-band structure for a given bit rate, given filter length, and given input signal correlation model. Specific design examples are worked out for 4-tap filters in the two-band paraunitary case. These theoretical results are confirmed by extensive Monte Carlo simulation.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.215-220
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• 2014

We consider quantization optimized for distributed estimation, where a set of sensors at different sites collect measurements on the parameter of interest, quantize them, and transmit the quantized data to a fusion node, which then estimates the parameter. Here, we propose an iterative quantizer design algorithm with a weighted distance rule that allows us to reduce a system-wide metric such as the estimation error by constructing quantization partitions with their optimal weights. We show that the search for the weights, the most expensive computational step in the algorithm, can be conducted in a sequential manner without deviating from convergence, leading to a significant reduction in design complexity. Our experments demonstrate that the proposed algorithm achieves improved performance over traditional quantizer designs. The benefit of the proposed technique is further illustrated by the experiments providing similar estimation performance with much lower complexity as compared to the recently published novel algorithms.