• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.281-284
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• 2000

This paper describes design of a (32, 28) Reed Solomon decoder for optical compact disk with double error detecting and correcting capability. A variety of error correction codes(ECCs) have been used in magnetic recordings, and optical recordings. Among the various types of ECCs, Reed Solomon(RS) codes has emerged as one the most important ones. The most complex circuit in the RS decoder is the part for finding the error location numbers by solving error location polynomial, and the circuit has great influence on overall decoder complexity. We use RAM based architecture with Euclid's algorithm, Chien search algorithm and Forney algorithm. We have developed VHDL model and peformed logic synthesis using the SYNOPSYS CAD tool. The total umber of gate is about 11,000 gates.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.391-399
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• 2006

We propose a novel low complexity video encoder, at the expense of a complex decoder, where video frames are intra-coded periodically and frames in between successive intra-coded frames are coded efficiently using a proposed irregular binning technique. We investigate a method of forming an irregular binning which is capable of quantizing any value effectively with only small number of bins, by exploiting the correlation between successive frames. This correlation is additionally exploited at the decoder, where the quality of reconstructed frames is enhanced gradually by applying POCS(projection on the convex sets). After an image frame is reconstructed with the irregular binning information at the proposed decoder, we can further improve the resulting quality by modifying the reconstructed image with motion-compensated image components from the neighboring frames which are considered to contain image details. In the proposed decoder, several iterations of these modification and re-projection steps can be invoked. Experimental results show that the performance of the proposed coding scheme is comparable to that of H.264/AVC coding in m mode. Since the proposed video coding does not require motion estimation at the encoder, it can be considered as an alternative for some versions of H.264/AVC in applications requiring a simple encoder.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.303-309
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• 2016

This paper presents a channel-adaptive rate control algorithm for low delay video coding. The main goal of the proposed method is to adaptively use the unknown available channel bandwidth while reducing the end-to-end delay between encoder and decoder. The key idea of the proposed algorithm is for the status of the encoder buffer to indirectly reflect the mismatch between the available channel bandwidth and the generated bitrate. Hence, the proposed method fully utilizes the unknown available channel bandwidth by monitoring the encoder buffer status. Simulation results show that although the target bitrate mismatches the available channel bandwidth, the encoder efficiently adapts the given available bandwidth to improve the peak signal-to-noise ratio.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.55-61
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• 2021

In this paper, we proposed a DeepLabv3+ based encoder-decoder model utilizing an attention mechanism for precise semantic segmentation. The DeepLabv3+ is a semantic segmentation method based on deep learning and is mainly used in applications such as autonomous vehicles, and infrared image analysis. In the conventional DeepLabv3+, there is little use of the encoder's intermediate feature map in the decoder part, resulting in loss in restoration process. Such restoration loss causes a problem of reducing segmentation accuracy. Therefore, the proposed method firstly minimized the restoration loss by additionally using one intermediate feature map. Furthermore, we fused hierarchically from small feature map in order to effectively utilize this. Finally, we applied an attention mechanism to the decoder to maximize the decoder's ability to converge intermediate feature maps. We evaluated the proposed method on the Cityscapes dataset, which is commonly used for street scene image segmentation research. Experiment results showed that our proposed method improved segmentation results compared to the conventional DeepLabv3+. The proposed method can be used in applications that require high accuracy.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.1
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• pp.1-11
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• 1999

In this paper, we proposed a design method of parameterized viterbi decoder, which automatically synthsizes the diverse viterbi deciders used in the digital mobile communication systems. It is designed to synthesize a viterbi decoder specified by user-provided parameters. Those parameters are constraint length, code rate generator polynomials of teh convolutional encoder, data rate and bits/frame of the data transmission, and soft decision bits of viterbi decoder. For the design of the parameterized viterbi decoder, we designed a user interface module C-language, and a viterbi decoder module in a hierarchical atructure using VHDL language and its generic statement. For the verification of the parameterized viterbi decoder, we compared our synthesized viterbi decoder with the conventional viterbi decoder which is designed for the IS-95 CDMA system. The proposed design method of the viterbi decoder will be a new method to obtain a required viterbi decoder in a very short time only by supplying the design parameters.

• Smart Media Journal
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• v.9 no.1
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• pp.16-22
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• 2020

In this study, a neural network method performing both Detection and Classification of diseases and insects in paprika is proposed with Multi-Tasking U-net. Paprika on farms does not have a wide variety of diseases in this study, only two classes such as powdery mildew and mite, which occur relatively frequently are made as the targets. Aiming to this, a U-net is used as a backbone network, and the last layers of the encoder and the decoder of the U-net are utilized for classification and segmentation, respectively. As the result, the encoder of the U-net is shared for both of detection and classification. The training data are composed of 680 normal leaves, 450 mite-damaged leaves, and 370 powdery mildews. The test data are 130 normal leaves, 100 mite-damaged leaves, and 90 powdery mildews. Its test results shows 89% of recognition accuracy.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.53-64
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• 2013

The existing research on the QoS of the Streaming video system(SVS) adjust the video stream being sent from the network perspective, the focus is to prevent data loss and delay. The other hand, changing the quality of the video stream will not receive the guaranteed QoS from the perspective of the users who want uniform quality. In order to solve these problems, this paper applied to ensure that the benefits in the amount of bits per frame occurs Encoder CBR streaming video is kept constant and uniform picture quality advantages of VBR streaming video VBR to CBR Encoder and CBR to VBR Decoder, Video was designed to control the playback stream for And to ensure a uniform quality of the user based on the design and implementation of uniform quality guaranteed SVS. PSNR evaluated for several characteristics of the sample video to demonstrate the superiority of the SVS ensure uniform quality, the proposed Performance evaluation of the SVS ensure uniform quality CBW the ABR from 100% to ensure uniform image quality from 85% to environmental excellence is proved.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.2120-2130
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• 1998

This paper concerns a design of programmable MPEG decoder for video processing by object unit on network. The decoder can process video data effectively by a embedded controller with stack buffers for supporting OOP (Object-Oriented Programming). The controller offers extended instructions that process several data types including 32bit integer type. In addition to that, we have a vector processor, in this decoder that can execute advanced compensation and prediction by half pixel and SA(Shape Adaptive)-IDCT of MPEG-4. Absolutors and halfers in the vector processor make this architecture extensive to a encoder. We verified the decoder with $0.6\mu\textrm{m}$ 5-Volt CMOS COMPASS library.

• ETRI Journal
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• v.44 no.1
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• pp.38-50
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• 2022

This paper presents a study on the use of encoder-derived features in decoder-side depth estimation. The scheme of multiview video encoding does not require the transmission of depth maps (which carry the geometry of a three-dimensional scene) as only a set of input views and their parameters are compressed and packed into the bitstream, with a set of features that could make it easier to estimate geometry in the decoder. The paper proposes novel recursive block splitting for the feature extraction process and evaluates different scenarios of feature-driven decoder-side depth estimation, performed by assessing their influence on the bitrate of metadata, quality of the reconstructed video, and time of depth estimation. As efficient encoding of multiview sequences became one of the main scopes of the video encoding community, the experimental results are based on the "geometry absent" profile from the incoming MPEG Immersive video standard. The results show that the quality of synthesized views using the proposed recursive block splitting outperforms that of the state-of-the-art approach.