• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.13-16
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• 2001

In the Dedicated Short Range Communication (DSRC) system channel, a large number of bit errors occur because of Additive White Gaussian Noise (AWGN) and fading. When an image data is transmitted under the condition, reconstructed image quality is significantly degraded. In this paper, as an alternative to the error correcting code and/or automatic repeat request scheme, we propose an error recovery scheme for image data transmission. We first analyze how transmission errors in the DSRC system channel degrade image quality. Then, in order to improve image quality, we propose error resilient and concealment schemes for still image transmission using DCT-based fixed length coding, hamming code, cyclic redundancy check, and interleaver. Finally, we show its performance by an experiment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.434-441
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• 2003

This paper proposes a new quantitative and objective image quality metric which is essential to verify the performance of block-based DCT image coding. The proposed metric considers not only global distortion of coded image such as spatial frequency sensitivity and channel masking using HVS based multi-channel model, but also structural distortions caused block-based coding. The experimental results show a strong correlation between proposed metric and subjective metric.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.434-434
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• 2003

This paper proposes a new quantitative and objective image quality metric which is essential to verify the performance of block-based DCT image coding The proposed metric considers not only global distortion of coded image such as spatial frequency sensitivity and channel masking using HVS based multi-channel model, but also structural distortions caused block-based coding. The experimental results show a strong correlation between propose(B metric and subjective metric.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.738-747
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• 2015

It is difficult to use a recognition algorithm of an image in a foggy environment because the color and edge information is removed. One of the famous defogging algorithm is haze removal by using 'Dark Channel Prior(DCP)' which is used to predict for transmission rate using color information of an image and eliminates fog from the image. However, in case that the image has factors such as sunset or yellow dust, there is overemphasized problem on the color of certain channel after haze removal. Furthermore, in case that the image includes an object containing high RGB channel, the transmission related to this area causes a misestimated issue. In this paper, we purpose an enhanced fog elimination algorithm by using improved color normalization and haze rate revision which correct mis-estimation haze area on the basis of color information and edge information of an image. By eliminating the color distortion, we can obtain more natural clean image from the haze image.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.173-176
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• 2000

The fact that the channel coding methode with property of the source coder is better than using the channel characteristics is well known. In this paper We propose the UEP channel coding scheme using IP-SCCC and DCT-based embedded coder for image transmission. Moreover, to evaluate the effectiveness of the proposed scheme some simulation results are presented, in which the channel models of additive white Gaussian noise and Rayleigh fading are assumed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.542-543
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• 2018

In this paper, we propose a method of visual information of original image by encrypting only partial data of infra-red image. In this method, the infra-red image is divided into R, G, and B channels, and the outline is searched for the three channel images. Then, an average value is calculated for each contour image, and a channel to be encrypted is selected. The wavelet transform is performed on the selected channel to encrypt only the LL subband in which the highest energy is concentrated. Experimental results show that the SSIM value is less than 0.09 in the case of the proposed encryption method.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.962-968
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• 2011

The conventional lossless compression of color images is to apply a compression method to each of color components separately, without considering the channel correlation. There had been several methods that consider the channel correlation, but they were confined to the compression of satellite or aerial images only, and the performance of these algorithms to general photos is not satisfactory. This paper proposes a new lossless color image compression method that exploits the correlation between the color components. Specifically, asymmetric sampling is applied to transform an image into mosaic image and the rest, which are compressed separately. By using the information from the compressed mosaic image, the rest images are predicted for further reducing the information to be compressed. Experimental results show that the proposed method improves the compression performance by 35% over the conventional separate compression methods and 10% over the existing methods that exploit the channel correlation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1181-1187
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• 2009

In this paper, we proposed a method to analyze kind of clouds using a fuzzy reasoning method. In the proposed method, we used the clues that G channel value is dominant from RGB color values in land areas and B channel value is dominant in the sea areas discovered by the analyses of both visible images and infrared images. By these information, R and B channel values are applied to land areas and R and G channel values are applied to the sea areas. Noise areas(areas except cloud areas) are removed from a visible image and an infrared image by a threshold value, and then land areas and the sea areas are discriminated from the noise removed image. Cloud areas are extracted from discriminated areas using R, G, B channel values and a fuzzy reasoning method, and finally kind of clouds is decided by combining same cloud areas included in both the visible image and the infrared image. In comparison with a conventional quantization method, we verified that the performance of cloud analysis by the proposed method is more efficient through experiments.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.97-105
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• 1994

Transform image coding using DCT is proved to be efficient in the absence of channel error but its performance degrades rapidly over noisy channel. In this paper, in the case of appling bit selcetive error correction coding that protects some significant bits in a codeword, an efficient allocation method of imformation bits and additive redundancy bits used for quantization and error correction coding respectively under constant transmission bit rate is proposed, and its performance is analyzed. As a result, without increasing trasmission bit rate, PSNR can be improved up to 7~8 [dB] below bit error rate $10^2$ and the image without blocking effect caused by bit error resulted from channel noise can be recostructed.