• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2341-2348
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• 1996

This paper proposes an image compression algorithm that adopts projection scheme on mean-residual metod. Sub-blocks of an image are encoded using mean-residual method where mean value is predicted according to that of neighboring blocks. Projection scheme with 8 directions is applied to the compression of residual signals of blocks. Projection vectors are finite-state vector quantized according to the projection angle of nighboring blocks in order to exploit the correlation among them. Side information to represent the repetition of projection is run-length coded while the information for projection direction is compressed using entropy encoding. The proposed scheme apears to be better in PSNR performance when compared with conventional projection scheme as well as in subjective quality preserving the edges of images better than most tranform methods which usually require heavy computation load.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.39-42
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• 2016

Since a display device such as TV or signage is getting larger, the types of media is getting changed into wider view one such as UHD, panoramic and jigsaw-like media. Especially, panoramic and jigsaw-like media is realized by stitching video clips, which are captured by different camera or devices. In order to stich those video clips, it is required to find out 2D Adjacency Matrix, which tells spatial relationships among those video clips. Discrete Cosine Transform (DCT), which is used as a compression transform method, can convert the each frame of video source from the spatial domain (2D) into frequency domain. Based on the aforementioned compressed features, 2D adjacency Matrix of images could be found that we can efficiently make the spatial map of the images by using DCT. This paper proposes a new method of generating 2D adjacency matrix by using DCT for producing a panoramic and jigsaw-like media through various individual video clips.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.296-301
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• 2006

Even though the emergency telecommunication make possible that specialist offers medical care over emergency cases in moving vehicle, we still have many problems in transmitting the image or video of patient over several wireless networks. To alleviate the effect of channel errors on compressed video bit-stream, this paper analyzed the error resilient features of JPEG2000 standard and measured the quality of transmission over noisy wireless channel, CDMA2000 1xEV-DO networks, compared to the features of error resilient tool of MPEG-4. We also proposed the optimum solution of transmitting images over real 3G network using JPEG2000 error resilient tool.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1668-1675
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• 2008

In this paper, we propose a post-processing technique developed for the subjective improvement of color resolution in DCT-coded color images. The high frequency components caused by complex object parts are compressed and impaired through DCT-based image processing, so color distortions such as blurs in high saturated regions are observed. It's mainly due to the severe loss of color data as Cb and Cr. Generally, the activities of chroma elements in DCT domain correlate strongly with that of luminance as spatial frequency gets higher, and based on the relations between chroma and luma AC activities, we compensate destructed Cb, Cr coefficients using modifications from Y coefficients. Simulation results show that the proposed method enhances color resolution in high saturated region, and improves the visual quality.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.257-260
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• 2000

In modern battlefield situation, radar and infrared sensors may be located on aircraft having limited computational resources available for real-time computer processing. Hence sensor images are transmitted typically to central stations for processing and automatic target recognition/detection. Owing to the limited bandwidth channels that are typically available between the aircraft and processing stations, images are compressed prior to transmission to facilitate rapid transfer. In this paper we examine the problem of compressing sensor data for transmission, given that target recognition is the end goal. Performance result shows that the front-end target recognition system achieves a relatively high level of performance as well as a high compression ratio.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.421-430
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• 2007

This paper presents the results of the ocean surface current velocity estimation using 6 Radarsat-1 SAR images acquired in west coastal area near Incheon. We extracted the surface velocity from SAR images based on the Doppler shift approach in which the azimuth frequency shift is related to the motion of surface target in the radar direction. The Doppler shift was measured by the difference between the Doppler centroid estimated in the range-compressed, azimuth-frequency domain and the nominal Doppler centroid used during the SAR focusing process. The extracted SAR current velocities were statistically compared with the current velocities from the high frequency(HF) radar in terms of averages, standard deviations, and root mean square errors. The problem of the unreliable nominal Doppler centroid for the estimation of the SAR current velocity was corrected by subtracting the difference of averages between SAR and HF-radar current velocities from the SAR current velocity. The corrected SAR current velocity inherits the average of HF-radar data while maintaining high-resolution nature of the original SAR data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4413-4425
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• 2020

In order to completely record all the information of realistic scenes, high dynamic range (HDR) images have been widely used in virtual reality, photography and computer graphics. A simple yet effective tone mapping method based on total variation is proposed so as to reproduce realistic scenes on low dynamic range (LDR) display devices. The structural component and texture component are obtained using total variation model in logarithmic domain. Then, the dynamic range of the structural component is compressed with an adaptive arcsine function. The texture component is processed by Taylor series. Finally, we adjust the saturation component using sigmoid function and restore the color information. Experimental results demonstrate that our method outperforms existing methods in terms of quality and speed.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.29-38
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• 2022

Although image compression is one of the essential technologies to transmit image data on a variety of surveillance and mobile healthcare applications, it causes unnecessary compression artifacts such as blocking and ringing artifacts by the lossy compression in the limited network bandwidth. Recently, image restoration methods using convolutional neural network (CNN) show the significant improvement of image quality from the compressed images. In this paper, we propose Image Denoising Convolutional Neural Networks (IDCNN) to reduce the compression artifacts for the purpose of improving the performance of object classification. In order to evaluate the classification accuracy, we used the ImageNet test dataset consisting of 50,000 natural images and measured the classification performance in terms of Top-1 and Top-5 accuracy. Experimental results show that the proposed IDCNN can improve Top-1 and Top-5 accuracy as high as 2.46% and 2.42%, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.112-115
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• 2022

The process of deep learning usually needs to deal with massive data which has greatly limited the development of deep learning technologies today. Convolutional Neural Network (CNN) structure is often used to solve image classification problems. However, a large number of images may be required in order to train an image in CNN, which is a heavy burden for existing computer systems to handle. If the image data can be compressed under the premise that the computer hardware system remains unchanged, it is possible to train more datasets in deep learning. However, image compression usually adopts the form of lossy compression, which will lose part of the image information. If the lost information is key information, it may affect learning performance. In this paper, we will analyze the effect of image compression on deep learning performance on CNN-based cat and dog classification. Through the experiment results, we conclude that the compression of images does not have a significant impact on the accuracy of deep learning.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.825-828
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• 2022

In this paper, we propose an efficient image size selection method for video-based point cloud compression. The current MPEG video-based point cloud compression reference encoding process configures a threshold on the size of images while converting point cloud data into images. Because the converted image is compressed and restored by the legacy video codec, the size of the image is one of the main components in influencing the compression efficiency. If the image size can be made smaller than the image size determined by the threshold, compression efficiency can be improved. Here, we studied how to improve the compression efficiency by selecting the best-fit image size generated during video-based point cloud compression. Experimental results show that the proposed method can reduce the encoding time by 6 percent without loss of coding performance compared to the test model 15.0 version of video-based point cloud encoder.