• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.623-627
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• 2009

Recently, we have many kinds of picture format and display, and resizing (scaling) of picture becomes important. In this processing, quality of picture depends on re-sizing method. For this, some methods to improve the PSNR have been proposed. However, subjective picture quality is more important. Especially, degradation caused by re-sizing, such as jaggy (aliasing) and ringing, should be reduced. To solve them, we have proposed the method using directional adaptive interpolation. To improve the performance of this method, we consider the shape analysis this time. In the proposed method, directional adaptive processing is applied for pure edge only. In the texture area and flat area, 8 tap re-sampling filter is used. As the results of processing, the reductions of jaggy and incorrect interpolated pixels are recognized. The subjective picture quality of proposed method is significantly better than 8-tap re-sampling which gives good PSNR.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.1
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• pp.81-87
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• 1988

This paper proposes a new EBTC(extended block truncation coding) algorithm extended from the BTC for image compression. The EBTC has a capability to eliminate the defects of BTC, such as the deterioration of resolution or blocky effect,and to make a real-time processing like BTC. It shows better performances than the DPCM and the transform coding. Especially, it is a suitable coding method for the high quality picture transmission. It may be adequate to the system of transmission rate of 30-50 Mbits/sec. The picture quality has been scarecely degraded with a vector quantization to the EBTC output at the bit rate of 1.25 bits/pel. The bit rate of the scalar quantized EBTC method is 2.6-3.7 bits/pel.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1154-1162
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• 2010

Because it is easy to implement the scalar quantizer, it is used in various video coding systems. Although the scalar quantizer with a large quantization stepsize can reduce the amount of data, it has disadvantage that the reconstructed picture quality is poor. In this paper, we propose an efficient method which improves the coding performance by maintaining original quantization stepsize and increasing the number of quantization reconstruction levels. Simultaneously, for the purpose of solving the problem of transmitting the added symbol informations which is used to indicate the region of quantizer reconstruction level as the number of quantizer reconstruction level is increased, we also suggest the method to reduce the added informations. Therefore, for the intra-coded picture of H.264 video coding system, we generate the huffman codes for the symbol informations of quantization reconstruction regions by 4${\times}$4(horizontal 4 pixels, vertical pixels) block unit. Furthermore, for the inter-coded picture, we also generate the huffman codes for the symbol informations of quantization reconstruction regions by 8${\times}$8 blocks and 4${\times}$4 blocks within a macroblock. Adopting this method of reducing the added information by increasing the number of quantization reconstruction region, It is shown that the coding performance can be improved at the same bitrate.

• Journal of the Ergonomics Society of Korea
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• v.17 no.1
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• pp.67-77
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• 1998

The objective of this study is to extract information from electroencephalogram(EEG) signals with which we can discriminate mental states. Seven university students were participated in this study. Ten stimuli based on IAPS (International Affective Picture Systems) Were presented at random according to the experimental schedule. 8-channel ($O_1$, $O_2$, $F_3$, $F_4$, $F_7$, $F_8$, $FP_1$, and $FP_2$)EEG signals were recorded at a sampling rate of 204.8 Hz for visual stimuli and analyzed. After random ten sequential stimuli presentation, the subject subjectively assessed the stimulus by scaling from -5 to 5. If the stimulus was the best and the worst, it was scored 5 and -5, respectively. Only maximum and minimum scored-EEG signals within each subject were selected on the basis of subjectively assessment for analysis. EEG signals were transformed into feature objects based on scalar autoregressive model coefficients. They were classified with Discriminant Analysis for each channel. The features produced results with the best classification accuracy of 85.7 % in $O_1$ and $O_2$ for visual stimuli. This study could be extended to establish an algorithm which quantify and classify emotions evoked by visual stimulus using autoregressive models.