• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.461-474
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• 2009

The cognitive radio (CR) technique is a useful tool for improving spectrum utilization by detecting and using the vacant spectrum bands in which cooperative spectrum sensing is a key element, while avoiding interfering with the primary user. In this paper, we propose a novel cluster-based cooperative spectrum sensing scheme in cognitive radio with two solutions for the purpose of improving in sensing performance. First, for the cluster header, we use the double adaptive energy thresholds and a multi-bit quantization with different quantization interval for improving the cluster performance. Second, in the common receiver, the weighed HALF-voting rule will be applied to achieve a better combination of all cluster decisions into a global decision.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.271-274
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• 1997

This paper presents an adaptive quantization of image sequences using the Radial Basis Function Network(RBFN) which classifies interframe image blocks. The clssification algorithm consists of two steps. Blocks are classified into NA(No Activity), SA(Small Activity), VA(Verical Activity), and HA(Horizontal Activity) classes according to edges, image activity and AC anergy distribution. RBFN is trained using the classification results of the above algorithm, which are nonlinear classification features are acquired from the complexity and variability of difference blocks. Simulation result shows that the the adaptive quantization using the RBFN method produced better results better results than that of the sorting and MLP methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.332-338
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• 2001

A visually adaptive quantization method of DCT-based images based on Human Visual System(HVS) is proposed. This approach uses the spatial masking in HVS characteristics to obtain higher compression ratio with relatively small degradation in the image quality. HVS is nonsensitive to an edge area, so a high complexity area is quantized coarsely in contrast to fine quantization of the low complexity area. The complexity of an area is estimated by the variance of DCT coefficients of the image. Experimental results demonstrate the performance of the proposed method and the resulting images show little difference from the original image in the subjective perception.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.18-27
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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. In this paper, such a lattice vector quantization is implemented by using the sample-adaptive product quantizer (SAPQ). It is shown that several important lattices can be implemented by SAPQ and the lattice vector quantization can be performed by using a simple integer-transform function of scalar values within SAPQ. The performance of the proposed lattice SAPQ is compared to the entropy-constrained scalar quantizer and the entropy-constrained SAPQ (ECSAPQ) at a similar encoding complexity. Even though ECSAPQ shows a good performance at low bit-rates, lattice SAPQ shows better performance than the ECSAPQ case for a wide range of bit rates.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.187-190
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• 2001

In this paper, we present a novel digital watermarking technique based on the concept of multiresolution decomposition and Human Visual System(HVS). Proposed watermarking is to embed watermark by quantization, that is to construct ‘perceptually lossless’quantization matrix, by using a quantization factor for each level and orientation and variance within a band. We compare our approach with another wavelet domain watermarking methods. Simulation results show the superior performance of robustness for variety image distortions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.79-86
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• 2003

A new quantization constraint set based on the theory of Projection onto Convex Set(POCS) is proposed to reduce blocking artifact appearing in block-coded images. POCS-based postprocessing for alleviating the blocking artifact consists of iterative projections onto smoothness constraint set and quantization constraint set, respectively. In general, the conventional quantization constraint set has the maximum size of range where original image data can be included, therefore over-blurring of restored image is unavoidable as iteration proceeds. The projection onto the proposed quantization constraint set can reduce blocking artifact as well as maintain the clearness of the decoded image, since it controls adaptively the size of quantization constraint set according to the DCT coefficients. Simulation results using the proposed quantization constraint set as a substitute for conventional quantization constraint set show that the blocking artifact of the decoded image can be reduced by the small number of iterations, and we know that the postprocessed image maintains the distinction of the decoded image.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.11
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• pp.933-940
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• 2021

The space-borne SAR(Synthetic Aperture Radar) system radiates the microwave signal and receives the backscattered signal. The received signal is converted to digital at the Digital Receiver, which is implemented at the end of the SAR sensor receiving chain. The converted signal is formated after signal processing such as filtering and data compression. Two quantization are conducted in the Digital Receiver. One quantization is an analog to digital conversion at ADC(Analog-Digital Converter). Another quantization is the BAQ(Block Adaptive Quantization) for data compression. The quantization process is a conversion from a continuous or higher bit precision to a discrete or lower bit precision. As a result, a quantization noise is inevitably occurred. In this paper, the impact of two quantization processes are analyzed in a view of SNR degradation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.194-195
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• 2019

In this paper, we propose a frequency location adaptive hard-decision quantization (HDQ) scheme for video coding. A threshold for zero quantized level is adaptively applied to unquantized transform coefficients based on its frequency location in the transform domain. The proposed method achieves an average of 1.13%, 1.57%, and 1.53% of bit-rate reduction in BDBR sense compared to the conventional HDQ scheme respectively in Y, Cb, and Cr under the all intra encoding configuration.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.243-252
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• 1989

In this study, medical images, which are X-ray image and CT image, are compressed by the adam live coding technique. The medical images may be treated as special ones, because they are different from general images in many respects. The statistical characteristics that medical images only have in transform domain are analyzed, and then the improved quantization method is proposed for medical images. For chest X-ray image and CT head image, the better results are obtained by the improved adaptive coding technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.540-549
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• 2013

An adaptive non-uniform quantization scheme is proposed for soft-decision error correction in NAND flash memory. Even though the conventional maximizing mutual information (MMI) quantizer shows the optimal post-FEC (forward error correction) bit error rate (BER) performance, this quantization scheme demands heavy computational overheads due to the exhaustive search to find the optimal parameter values. The proposed quantization scheme has a simple structure that is constructed by only six parameters, and the optimal values of them are found by maximizing the mutual information between the input and the output symbols. It is demonstrated that the proposed quantization scheme improves the BER performance of soft-decision decoding with only small computational overheads.