- M.S. - Intelligent Visual Computing Lab, Sejong Univ.2021.03 – Present
- B.S. - School of Intelligent Mechatronics Engineering, Sejong University, Feb. 2021
- VideoCoding [VVC,MPEG-I(MIV),VCM]
- DeepLearning [SuperResolution,S2VT]
- C++ / Python
- Video Understanding (TMIV/VCM/VTM)
- pytorch, tensorflow2
- ffmpeg(OpenSW)
- Docker, git management
- *“Immersive Video Coding Using Deep Learning Based Specular Detection”, Association for the Advancement of Artificial Intelligence (Under Review)
- “Specular Detection and Rendering for Immersive Multimedia”, IEEE Multimedia (Major Revisions)
- “Efficient Video Captioning Using Quality Enhancement in Video Communication Systems”, Expert Systems with Applications (Under Review)
- “Low-Complexity Two-Step Lossless Depth Coding Using Coarse Lossy Coding”, Multimedia Tools and Applications, Apr. 2022
- “Low-Complexity Intra Coding in Versatile Video Coding”, IEEE Trans. Consumer Electronics, May 2022
*:First Order
- *"몰입형 입체 영상 부호화를 위한 VVC 인루프 필터 성능 분석", 2022 한국방송미디어공학회 추계학술대회
- *[MPEG-I] “Future MPEG Immersive Video Coding Based on Specular Detection”, m57981, Oct.2021
- *[MPEG-I] “Deep Learning Based Specular Pruning”, m58997, Jan.2022
- [MPEG-I] "The crosscheck report for EE4.a in Future MIV exploration experiment", m56611, April.2021
- [MPEG-I] "Results for EE3 on Future MIV", m57492, July.2021
- [VVC] “AHG11:Deep Neural Network for Super-Resolution”, JVET-T0096, Oct.2020
- [VCM] “VVC tool combination for efficient feature map coding”, m60128, Jul.2022
- [VCM] “Performance analysis of VVC inter tools for feature map coding”, m60127, Jul.2022
- [VCM] “Performance analysis of VVC intra tools for feature map coding”, m60126, Jul.2022
*:First Order
- [출원] “영상의 화질에 따라 초해상도 딥러닝 네트워크를 적용하는 비디오 처리 방법 및 비디오 처리 장치”, 10-2022-0011541
- More_Detail(Not ready) : https://chldydgh4687.github.io