• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.62-76
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• 1994

In this paper, a segmentation-based coding technique as applied to video sequences is proposed. A proposed method separates an image into contour and texture parts, then the visually-sensitive contour part is represented by chain codes and the visually-insensitive texture part is reconstructed by a representative motion vector of a region and mean of the segmented frame difference. It uses a change detector to find moving areas and adopts variable blocks to represent different motions correctly. For better quality of reconstructed images, the displaced frame difference between the original image and the motion compensated image reconstructed by the representative motion vector is segmented. Computer simulation with several video sequences shows that the proposed method gives better performance than the conventional ones in terms of the peak signal to noise ratio(PSNR) and compression ration.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.911-914
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• 1998

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details shouuld be coded in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological laplacian operator and modified post-it transform is very efficient for improving quality of the reconstructed images.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.460-467
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• 2002

This paper proposes new motion vector re-estimation techniques for transcoding of heterogenous video coders. The first case is for transcoder of coding format. The coding algorithm with B-picture like MPEG standards is transcoded into the different algorithm without B-picture like H.261 standards. The second case is for transcoder of spatial resolution reduction. It generates a downscaled video bitstream at the video source. The third case is the integrated method that previous two cases are needed simultaneously. These three transcoding algorithms are compared and verified though reconstructed image quality in PSNR, motion vector errors between optimal motion vectors of cascaded trans- coder and motion vectors estimated by proposed methods and the amount of operation.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1972-1975
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• 2003

Video segmentation is an essential part in region-based video coding and any other fields of the video processing. Among lots of methods proposed so far, the watershed method in which the region growing is performed for the gradient image can produce well-partitioned regions globally without any influence on local noise and extracts accurate boundaries. But, it generates a great number of small regions, which we call over segmentation problem. Therefore we proposes image segmentation improvement by selective application structuring element of mathematical morphology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.17-32
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• 1996

In this paper, a model for two-layered video traffic is proposed. The performance analysis of the proposed model and the effects of two-layer coding scehemes in ATM networks are also studied. ATM-based networks give the possibility to support image codingat variable bit rate(VBR). Two layer coding is one of the very promising methods among many proposed methods to compensate the cell loss, the major drawback in ATM networks. From the experimental data of the 2-layer coded video traffics, it is observed that traffic patterns of base layer and enhanced layer are highly correlate to each other, when constant image quality is kept. With this observation, coded two layered video traffic can be modeled as 2-dimensional Markov chain. The model well fit the real experimental data. The model was used for the analysis of the performance of statistical multiplexer with priorites in ATM networks.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.276-280
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• 2017

This paper presents computationally efficient image compression for wireless transmission of high-definition video, to adaptively utilize available channel bandwidth and improve image quality. The method indirectly predicts an unknown available channel bandwidth by monitoring encoder buffer status, and adaptively controls a quantization parameter to fully utilize the bandwidth. Experimental results show that the proposed method is robust to variations in channel bandwidth.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.265-274
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• 1999

In this paper, we propose a novel interframe coding algorithm especially appropriate for 3-D medical images. The proposed algorithm is based on a video coding algorithm using motion estimation/ compensation and transform coding. In the algorithm, warping is adopted lor motion compensation (MC). Then, by using adaptive mode selection, a motion compensated residual image and original image are mixed up in the wavelet transform domain for improvement in coding performance. The mixed image is then compressed by the zerotree coding method. We prove that the adaptive mode selection technique in the wavelet transform domain is very useful lor 3-D medical image coding. Simulation results show that the proposed scheme provides good performance regardless of inter-slice distance and is prospective for 3-D medical image compression.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.318-327
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• 2002

This paper Introduces a new scanning method for network-adaptive scalable streaming video coding methodologies such as the MPEG-4 Fine Granular Scalable (FGS) Coding. Proposed scanning method can guarantee the subjectively improved picture quality of the region of the interest in the decoded video by managing the image information of that interested region to be encoded and transmitted most-preferentially, and also to be decoded most-preferentially. Proposed scanning method can lead the FGS coding method to achieve improved picture quality, in about 1dB ~ 3dB better, especially on the region of interest.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.211-214
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• 2001

This paper presents an efficient fast motion estimation algorithm and image segmentation method for low bit-rate coding. First, with region split information, the algorithm splits the image having homogeneous and semantic regions like face and semantic regions in image. Then, in these regions, We find the motion vector using adaptive search window adjustment. Additionally, with this new segment based fast motion estimation, we reduce blocking artifacts by intensively coding our interesting region(face or arm) in input image. The simulation results show the improvement in coding performance and image quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.650-652
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• 2022

Frame rate up-conversion (FRUC) algorithm is an image interpolation technology that improves the frame rate of moving pictures. This solves problems such as screen shake or blurry motion caused by low frame rate video in high-definition digital video systems, and provides viewers with a more free and smooth visual experience. In this paper, we propose a video compression technique using deep learning-based FRUC algorithm. The proposed method compresses and transmits after excluding some images from the original video, and uses a deep learning-based interpolation method in the decoding process to restore the excluded images, thereby compressing them with high efficiency. In the experiment, the compression performance was evaluated using the decoded image and the image restored by the FRUC algorithm after encoding the video by skipping 1 or 3 pages. When 1 and 3 sheets were excluded, the average BD-rate decreased by 81.22% and 27.80%. The reason that excluding three images has lower encoding efficiency than excluding one is because the PSNR of the image reconstructed by the FRUC method is low.