• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.174-183
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• 2014

This paper is to propose a scalable video coding scheme for adaptive digital hologram video service for various reconstruction environments. It uses both the light source information and digital hologram at both the sending side and the receiving side. It is a resolution-scalable coding method that scales the resolution, that is, the size of the reconstructed image. The method compresses the residual data for both the digital hologram and the light source information. For the digital hologram, a lossy compression method is used, while for the light source information, a lossless compression method is used. The experimental results showed that the proposed method is superior to the existing method in the image quality at the same compression ratio. Especially it showed better performance than the existing method as the compression ratio becomes higher.

• 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].

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.77-83
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• 2012

In this paper, a low-complexity embedded compression (EC) Codec algorithm for the wavelet video coder is applied to reduce excessive external memory requirements. The EC algorithm is used to achieve a fixed compression ratio of 50 % under the near-lossless-compression constraint. The EC technique can reduce the 50 % memory requirement for intermediate low-frequency coefficients during multiple discrete wavelet transform stages compared with direct implementation of the wavelet video encoder of this paper. Furthermore, the EC scheme based on a forward adaptive quantization and fixed length coding can save bandwidth and size of buffer between DWT and SPIHT to 50 %. Simulation results show that our EC algorithm present only PSNR degradation of 0.179 and 0.162 dB in average when the target bit-rate of the video coder are 1 and 0.5 bpp, respectively.

• Convergence Security Journal
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• v.9 no.2
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• pp.79-84
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• 2009

Recently, Move Picture Files have the same file format and a compression technique as Window Media Video form. but Moving Pictures using file format and a compression technique have question about Motion blur and compressibility. In this paper, we design and Implement for new Move Picture Solution EZ-MOV using FLV different from developed FLV(Flash Video) in the Macromedia company. EZ-MOV have advantages as follow. first, FLV player is able to compact disk access time and DRM (Digital Rights Management) with a built-in self and unable to an illegal video recording, second, whenever WMV formal file encoded FLV are able to lossless compression to fifty percent, third, FLV is able to Moving Picture streaming no buffering. fourth, FLV file is able streaming service no streaming server. fifth, FLV file is able to streaming service keep pace with download and streaming. sixth, FLV file is able to full duplex service.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.