ScholarWorks@Hanyang University ERICA Campus: Ko, Hyunsuk

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Ko, Hyunsuk (고현석)

ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)

Research Area: 영상압축, 인공지능 / 딥러닝 / 머신러닝, 영상 인지화질 측정 및 개선, Video Coding, AI / Deep learning / Machine learning, Perceptual Image/Video Quality Assessment and Improvement

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Image coding:3||video compression:3||perceptual video coding (PVC):2||Detection of Envelope Modulation on Noise (DEMON):2||Machine learning:2||Interest point:2||quantization distortion:2||Video quality:2||CU depth decision:2||natural video statistics:2||Intraprediction:2||statistical regularity:2||H.264/AVC:2||Just noticeable distortion (JND):2||perceptual quality:2||Deep learningbased compression:1||Deep neural network:1||DeepNetworkbased segmentation model:1||Digital holography:1||Disparitycompensation:1||Encoding:1||Epipolar geometry:1||Facial image deblurring:1||Facial landmark detection:1||Facial occlusion:1||Feature extraction:1||Frequency line extraction:1||Fullcomplex hologram:1||Fundamental matrix:1||GAN:1||Gallium nitride:1||Generative adversarial networks:1||Geometric distortion:1||HEVC:1||HEVC (high efficiency video coding):1||High dynamic range:1||Hologram compression:1||Holography:1||Homography:1||Illuminance enhancement:1||Image databases:1||Image manipulation:1||Image processing:1||Image quality:1||Image quality assessment:1||Image quality database:1||Image reconstruction:1||Interprediction:1||Intra prediction:1||Keypoint:1||L0norm minimization:1||Low detectable underwater target tracking:1||Modedecision:1||Multiview Video Coding (MVC):1||Objective quality assessment:1||Projective rectification:1||Quality assessment:1||Ratedistortion optimization:1||Ratedistortion optimization (RDO):1||Regularization:1||SVD:1||Semantic segmentation:1||Spatial databases:1||Spatial resolution:1||Standards:1||Stereoscopic images:1||Stitching:1||Streaming media:1||Threedimensional displays:1||Video quality database:1||Video recording:1||Viewscalability:1||asymmetric distortion:1||bearing–time record image:1||bilateral filter:1||binocular perception model:1||blob detection:1||class imbalance:1||convolution neural network (CNN):1||coronary arterial disease:1||coronary arterial lesion detection from coronary computed tomography angiography:1||coronary computed tomography angiography:1||deep learningbased compression:1||deeplearningbased image segmentation:1||depth image based rendering:1||depthmap coding:1||displaced frame difference:1||edgepreserving reference sample filter:1||high efficiency video coding:1||human study:1||image feature extraction:1||image quality assessment:1||image quality database:1||keypoint:1||learningbased detection:1||machine learning:1||multiple reference sample lines:1||network training loss function:1||nonconstant luminance:1||passive SONAR:1||phaseonly hologram:1||preprocessing:1||preprocessing of video coding:1||quality index:1||spacetime displaced frame differences:1||spacetime resolution:1||spacetime subsampled video coding:1||spatiotemporal resolution:1||stereoscopic images:1||structural distortion:1||structured learning:1||subjective quality:1||subjective test:1||support vector machines:1||support vector regression:1||the generative image database:1||video coding and processing:1||video quality assessment:1||view synthesis:1||wide color gamut:1||딥러닝:1||의미론적 분할:1||3D:1||주파수선 추출:1||3D images:1||Bearingtime records image:1||Bit rate:1||Boundary filtering:1||Computedgenerated Hologram (CGH):1||Computedgenerated hologram(CGH):1||Computer graphics:1||Computing methodologies:1||Constrained optimization:1||DEMON 그램:1||DEMONgram:1||Databases:1||Decision fusion:1||Deep learning:1||

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