• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1910-1924
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• 1994

Curved 3D objects represented by range data contain large amounts of information compared with planar objects, but do not have distinct features for matching to those of object models. This makes it difficult to represent and identify a general 3D curved object. This paper introduces a new view-point independent approach to recognizing general 3D curved objects using range data. Our approach makes use of the relative geometric differences between particular points on the object surface and some model points. The model points are prespecified arbitrarily and keeping the task in mind so that the following task can be easily described using the model points. Our approach has several advantages. Since model points are specified arbitrarily and task dependently, further processing can be reduced in application by locating the model points at places which are useful for further operations in the task. The knowledge base is simple with less storage requirement. And, it is easy to compensate the uncertainties of positions estimation caused by noise and quantization error.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.321-328
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• 2008

In this paper, we propose the hardware architecture of two-dimensional discrete wavelet transform (2D DWT) and quantization for using JPEG2000. Color 2-D DWT processor is proposed that is to apply to JPEG 2000 Hard-wired Encoder. JPEG 2000 DWT processor uses the Daubechies' (9,7) bi-orthogonal filter, and we design by minimizing error of the DWT transformer by ${\pm}1$ LSB during compression and decompression. We designed the DWT filters that using by using shift and adder structure instead of multiplier structure which raise the hardware complexity. It is improve the operation speed of filters and reduce the hardware complexity. The proposed system is designed by the hardware description language Verilog-HDL and verified by Synopsys Design Analyzer using TSMC 0.25${\mu}m$ ASIC library.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.3
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• pp.292-299
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• 2015

An envelope elimination and restoration transmitter that uses a tri-level envelope encoding scheme is presented for improving the efficiency and linearity of the system. The proposed structure amplifies the same magnitude signal regardless of the input peak-to-average power ratio and reduces the quantization noise by spreading out the noise to the out-of-band frequency, resulting in the enhancement of power efficiency. An improved linearity is also obtained by providing a new timing mismatch calibration technique between the envelope and phase signal. Implementation in a 130 nm CMOS process, transmitter measurements on a 20-MHz long-term evolution input signal show an error vector magnitude of 3.7 % and an adjacent channel leakage ratio of 37.5 dBc at 2.13 GHz carrier frequency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1689-1703
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• 2020

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.5
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• pp.1299-1309
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• 1998

In this paper, we propose a new approach for reducing the blocking artifact that is one of drawbacks of the block-based Discrete Cosine Transform (DCT) without introducing additional information or significant blurring. We modify the inter-block discontinuity minimization technique to preserve edges within a block as well as to reduce visible block boundaries. The homogeneity of each block is decided by the threshold value reated to Q-factor, which is included in a JPEG as well as MPEG streams. The quantization error is estimated by minimizing the discontinuity, which is weighted in proportion to block discontinuity and added to each pixel in the block to compensate block artifacts. The proposed algorithm reconstructs images which have less noticeable block boundaries from a subjective viewpoit without anyconstraints.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.917-923
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• 2011

In this paper, a new prediction based embedded compression (EC) codec algorithm for the JPEG2000 encoder system is proposed to reduce excessive memory requirements. The EC technique can reduce the 50 % memory requirement for intermediate low-frequency coefficients during multiple discrete wavelet transform (DWT) stages compared with direct implementation of the DWT engine of this paper. The LOCO-I predictor and MAP are widely used in many lossless picture compression codec. The proposed EC algorithm use these predictor which are very simple but surprisingly effective. The predictive EC scheme adopts a forward adaptive quantization and fixed length coding to encoding the prediction error. Simulation results show that our LOCO-I and MAP based EC codecs present only PSNR degradation of 0.48 and 0.26 dB in average, respectively. The proposed algorithm improves the average PSNR by 1.39 dB compared to the previous work in [9].

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.387-396
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• 2009

No-Reference video-quality assessments are divided into two kinds of metrics based on decoding pixel domain or the bitstream one. Traditional full-/reduced- reference methods have difficulty to be deployed as realtime video transmission because it has problems of additional data, complexity, and assessment accuracy. This paper presents simple and highly accurate no-reference video-quality assessment in realtime video transmission. Our proposed method uses quantization parameter, motion vector, and information of transmission error. To evaluate performance of the proposed algorithm, we perform subjective test of video quality with the ITU-T P.910 Absolute Category Rating(ACR) method and compare our proposed algorithm with the subjective quality assessment method. Experimental results show the proposed quality metric has a high correlation (85%) in terms of subjective quality assessment.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.853-867
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• 2010

Recently, thumbnail techniques are required to index a high-performance video at digital convergence-based multimedia service like IPTV and DMB. Therefore a thumbnail extraction method in H.264/AVC bitstreams has been proposed. However, thumbnail quality deterioration problem at converting the general equation of spatial domain to frequency domain which is generated by not considering about H.264/AVC transform and quantization processing and rounding-off operation in intra prediction. In this paper, we propose a new thumbnail extraction method in H.264/AVC bitstreams. The proposed scheme is based on H.264/AVC core-transform for a thumbnail extraction in frequency domain, and probability theory, intra rounding-off error compensation. Through the implementation and performance evaluation, the subjective quality difference between the output of our scheme and the output of reference decoder is negligible and better than the conventional method, and moreover PSNR gain by up to 8.66 dB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.585-593
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• 2017

In this paper, we propose a deblocking filter hardware architecture for low-power HEVC (High-Efficiency Video Coding) in-loop for mobile systems. HEVC performs image compression on a block-by-block basis, resulting in blockage of the image due to quantization error. The deblocking filter is used to remove the blocking phenomenon in the image. Currently, UHD video service is supported in various mobile systems, but power consumption is high. The proposed low-power deblocking filter hardware structure minimizes the power consumption by blocking the clock to the internal module when the filter is not applied. It also has four parallel filter structures for high throughput at low operating frequencies and each filter is implemented in a four-stage pipeline. The proposed deblocking filter hardware structure is designed with Verilog HDL and synthesized using TSMC 65nm CMOS standard cell library, resulting in about 52.13K gates. In addition, real-time processing of 8K@84fps video is possible at 110MHz operating frequency, and operation power is 6.7mW.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1614-1632
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• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.