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스테레오스코픽 3차원 지상파 방송을 위한 합동 비트율 제어 연구
장용준(Chang Yongjun),김문철(Kim Munchurl) 한국방송·미디어공학회 2010 한국방송공학회 학술발표대회 논문집 Vol.2010 No.11
Following the proliferation of three-dimensional video contents and displays, many terrestrial broadcasting companies prepare for starting stereoscopic 3DTV service. In terrestrial stereoscopic broadcast, it is a difficult task to code and transmit two video sequences while sustaining as high quality as 2DTV broadcast attains due to the limited bandwidth defined by the existing digital TV standards such as ATSC. Thus, a terrestrial 3DTV broadcasting system with heterogeneous video coding systems is considered for terrestrial 3DTV broadcast where the left image and right images are based on MPEG-2 and H.264/AVC, respectively, in order to achieve both high quality broadcasting service and compatibility for the existing 2DTV viewers. Without significant change in the current terrestrial broadcasting systems, we propose a joint rate control scheme for stereoscopic 3DTV service. The proposed joint rate control scheme applies to the MPEG-2 encoder a quadratic rate-quantization model which is adopted in the H.264/AVC. Then the controller is designed for the sum of two bit streams to meet the bandwidth requirement of broadcasting standards while the sum of image distortions is minimized by adjusting quantization parameter computed from the proposed optimization scheme. Besides, we also consider a condition on quality difference between the left and right images in the optimization. Experimental results demonstrate that the proposed bit rate control scheme outperforms the rate control method where each video coding standard uses its own bit rate control algorithm in terms of minimizing the mean image distortion as well as the mean value and the variation of absolute image quality differences.
딥러닝 기반 EEG-to-fMRI 생성에 관한 예비연구
이규석(Gyuseok Lee),마히마 아리아(Arya Mahima),앙드레 브레흐만(Andre Brechmann),요르그 스타들러(Jorg Stadler),장용준(Yongjun Chang),유원상(Wonsang You) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.11
Electroencephalography (EEG) measures the electrophysiological activity of the brain, while functional magnetic resonance imaging (fMRI) detects changes in blood flow in the brain. Recently, multimodal brain imaging where both modalities are simultaneously taken and jointly analyzed has increasingly attracted for neuroscientific research. However, its use is still limited due to the cost of the technology and difficulties in integrating forms. In this paper, we report our pilot study on generating fMRI data from EEG using deep learning. We trained U-net for generating fMRI data from EEG, and evaluated the accuracy of predicted fMRI data compared to ground truth quantitatively and qualitatively. Although our study is still ongoing, it exhibits the feasibility and applicability of EEG-to-fMRI synthesis technology for neuroscientific research based on multi-modal imaging.