• Journal of KIISE:Information Networking
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• 제37권5호
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• pp.363-373
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• 2010

The existing QoS mechanisms for video streaming are short of the consideration for various user environments and the characteristic of streaming applying programs. In order to overwhelm this problem, studies on the video streaming protocols exploiting scalable video coding (SVC), which provide spatial, temporal, and qualitative scalability in video coding, are progressing actively. However, these protocols also have the problem to deepen network congestion situation, and to lower fairness between other traffics, as they are not equipped with congestion control mechanisms. SVC based streaming protocols also have the problem to overlook the property of videos encoded in SVC, as the protocols transmit the streaming simply by extracting the bitstream which has the maximum bit rate within available bandwidth of a network. To solve these problems, this study suggests TCP-friendly network adaptive SVC streaming(T-NASS) protocol which considers both network status and SVC bitstream property. T-NASS protocol extracts the optimal SVC bitstream by calculating TCP-friendly transmission rate, and by perceiving the network status on the basis of packet loss rate and explicit congestion notification(ECN). Through the performance estimation using an ns-2 network simulator, this study identified T-NASS protocol extracts the optimal bitstream as it uses TCP-friendly transmission property and perceives the network status, and also identified the video image quality transmitted through T-NASS protocol is improved.

• Journal of Broadcast Engineering
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• 제21권6호
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• pp.929-943
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• 2016

This paper proposes the rate control algorithm based on the S-JND (Saliency-Just Noticeable Difference) model for considering perceptual visual quality. The proposed rate control algorithm employs the S-JND model to simultaneously reflect human visual sensitivity and human visual attention for considering characteristics of human visual system. During allocating bits for CTU (Coding Tree Unit) level in a rate control, the bit allocation model calculates the S-JND threshold of each CTU in a picture. The threshold of each CTU is used for adaptively allocating a proper number of bits; thus, the proposed bit allocation model can improve perceptual visual quality. For performance evaluation of the proposed algorithm, the proposed algorithm was implemented on HM 16.9 and tested for sequences in Class B and Class C under the CTC (Common Test Condition) RA (Random Access), Low-delay B and Low-delay P case. Experimental results show that the proposed method reduces the bit-rate of 2.3%, and improves BD-PSNR of 0.07dB and bit-rate accuracy of 0.06% on average. We achieved MOS improvement of 0.03 with the proposed method, compared with the conventional method based on DSCQS (Double Stimulus Continuous Quality Scale).

• Journal of Broadcast Engineering
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• 제5권2호
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• pp.247-259
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• 2000

This paper introduces a new technique that reduces the search times and Improves the accuracy of motion estimation using high temporal and spatial correlation of motion vector. Instead of using the fixed first search Point of previously proposed search algorithms, the proposed method finds more accurate first search point as to compensating searching area using high temporal and spatial correlation of motion vector. Therefore, the main idea of proposed method is to find first search point to improve the performance of motion estimation and reduce the search times. The proposed method utilizes the direction of the same coordinate block of the previous frame compared with a block of the current frame to use temporal correlation and the direction of the adjacent blocks of the current frame to use spatial correlation. Based on these directions, we compute the first search point. We search the motion vector in the middle of computed first search point with two fixed search patterns. Using that idea, an efficient adaptive predicted direction search algorithm (APDSA) for block matching motion estimation is proposed. In the experimental results show that the PSNR values are improved up to the 3.6dB as depend on the Image sequences and advanced about 1.7dB on an average. The results of the comparison show that the performance of the proposed APDSA algorithm is better than those of other fast search algorithms whether the image sequence contains fast or slow motion, and is similar to the performance of the FS (Full Search) algorithm. Simulation results also show that the performance of the APDSA scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS algorithm.

• Journal of the Institute of Convergence Signal Processing
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• 제16권4호
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• pp.149-155
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• 2015

Tooth implant is located in oral cavity and affects neck, skull base, and facail image. These magnetic inhomogeneities are usually frequency encoding direction which cause artifacts due to change of signal strength and geometric distortion. First, to evaluate signal to noise ratio (SNR) of magnetic resonance image caused by tooth implant this study uses meat phantom which is similar to human body and is consisted with fat, muscle, and water to measure signal to noise ratio. Second, signal to noise ratio by using custom-made fixed phantom is measured, and then signal to noise ratio size of different tooth implant types is compared and analyzed. The measured signal to noise ratio values of Brushite, HSA, Metal, and RBM for meat phantom were 2.76, 2.22, 1.88, and 1.57 on T1 SE, 1.88, 1.78, 1.65, and 1.79 on T2 FLAIR, 2.28, 2.25, 2.88, and 2.05 on T2 FSE, and 2.74, 1.94, 1.67, and 1.48 on T2 GRE. The measured signal to noise ratio values of Brushite, HSA, Metal, and RBM for fixed water phantom were 1.2, 1.06, 1.12, and 1.22 on DWI, 1.93, 1.87, 1.93, and 2.06 T1 SE, 1.83, 1.76, 1.82, and 1.92 on T2 FLAIR, 1.85, 1.79, 7.86, and 1.97 on T2 FSE, and 1.97, 1.93, 1.99, and 2.06 on T2 GRE. By considering through the results, patients and dentists need to consider some impacts from testing many aspects although their main purpose of having tooth implants is a dental restoration. Moreover, depending on the tooth implant characteristics of individual patients this study results can be used as baseline data when choosing test protocol.

• Korean Journal of Cognitive Science
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• 제28권4호
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• pp.193-221
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• 2017

Grouping episodes into semantically related categories is necessary for better mnemonic structure. However, the effect of grouping on memory of subordinate details was not clearly understood. In an fMRI study, we tested whether attending superordinate during semantic association disrupts or enhances subordinate episodic details. In each cycle of the experiment, five cue words were presented sequentially with two related detail words placed underneath for each cue. Participants were asked whether they could imagine a category that includes the previously shown cue words in each cycle, and their confidence on retrieval was rated. Participants were asked to perform cued recall tests on presented detail words after the session. Behavioral data showed that reaction times for categorization tasks decreased and confidence levels increased in the third trial of each cycle, thus this trial was considered to be an important insight where a semantic category was believed to be successfully established. Critically, the accuracy of recalling detail words presented immediately prior to third trials was lower than those of followed trials, indicating that subordinate details were disrupted during categorization. General linear model analysis of the trial immediately prior to the completion of categorization, specifically the second trial, revealed significant activation in the temporal gyrus and inferior frontal gyrus, areas of semantic memory networks. Representative Similarity Analysis revealed that the activation patterns of the third trials were more consistent than those of the second trials in the temporal gyrus, inferior frontal gyrus, and hippocampus. Our research demonstrates that semantic grouping can cause memories of subordinate details to fade, suggesting that semantic retrieval during categorization affects the quality of related episodic memory.