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Young Kyun Jang - IEEE Xplore Author Profile
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Young Kyun Jang

Also published under: Yeong Il Jang

Publications
10
Citations
130
Publications by Year
20192022
Co-Authors:
Show All Co-Authors (9)
Image of author Young Kyun Jang

Young Kyun Jang

Also published under: Yeong Il Jang

Affiliation

Dept. of Electrical & Computer Eng., INMC, Seoul National Univ.

Publication Topics

Biography

Yeong Il Jang (Graduate Student Member, IEEE) received the B.S. degree from Seoul National University, Seoul, South Korea, in 2017, where he is currently pursuing the Ph.D. degree. His research interests include image restoration, image compression, and computer vision.(Based on document published on 14 June 2021).
Publications
10
Citations
130
Publications by Year
20192022
Co-Authors:
Show All Co-Authors (9)
Author's Published Works

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Self-Supervised Pretraining for Deep Hash-Based Image Retrieval

Haeyoon Yang;Young Kyun Jang;Isaac Kang;Nam Ik Cho

2022 IEEE International Conference on Image Processing (ICIP)
Year: 2022 | Conference Paper |
Deep hashing aims to produce discriminative binary hash codes for fast image retrieval through a deep baseline network and additional trainable hash function. In a supervised deep hashing network, the baseline network is generally initialized with classification-based pretrained models, and the overall hashing network is trained in a supervised fashion. However, since classification and retrieval ...Show More

Self-Supervised Pretraining for Deep Hash-Based Image Retrieval

Haeyoon Yang;Young Kyun Jang;Isaac Kang;Nam Ik Cho

Year: 2022 | Conference Paper |

LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network

Hochang Rhee;Yeong Il Jang;Seyun Kim;Nam Ik Cho

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (14)
Recent learning-based lossless image compression methods encode an image in the unit of subimages and achieve comparable performances to conventional non-learning algorithms. However, these methods do not consider the performance drop in the high-frequency region, giving equal consideration to the low and high-frequency areas. In this paper, we propose a new lossless image compression method that ...Show More

LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network

Hochang Rhee;Yeong Il Jang;Seyun Kim;Nam Ik Cho

Year: 2022 | Conference Paper |

Disentangled Feature-Guided Multi-Exposure High Dynamic Range Imaging

Keuntek Lee;Yeong Il Jang;Nam Ik Cho

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Multi-exposure high dynamic range (HDR) imaging aims to generate an HDR image from multiple differently exposed low dynamic range (LDR) images. It is a challenging task due to two major problems: (1) there are usually misalignments among the input LDR images, and (2) LDR images often have incomplete information due to under-/over-exposure. In this paper, we propose a disentangled feature-guided HD...Show More

Disentangled Feature-Guided Multi-Exposure High Dynamic Range Imaging

Keuntek Lee;Yeong Il Jang;Nam Ik Cho

Year: 2022 | Conference Paper |

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval

Young Kyun Jang;Nam Ik Cho

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (44)
Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems. By fully exploiting label annotations, they are achieving outstanding retrieval performances compared to the conventional methods. However, it is painstaking to assign labels precisely for a vast amount of training data, and also, the annotation process is error-prone. To tackle t...Show More

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval

Young Kyun Jang;Nam Ik Cho

Year: 2021 | Conference Paper |

Lossless Image Compression Based on Image Decomposition and Progressive Prediction Using Convolutional Neural Networks

Jae Hoon Shim;Hochang Rhee;Yeong Il Jang;Geonsu Lee;Seyun Kim;Nam Ik Cho

2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
This paper presents a lossless image compression method based on the image decomposition and progressive prediction of decomposed images and their coding contexts using convolutional neural networks (CNNs). We first decompose a given input into sub-images by sub-sampling into horizontal and vertical directions. The first sub-image is encoded by an existing non-learning lossless compressor, and the...Show More

Lossless Image Compression Based on Image Decomposition and Progressive Prediction Using Convolutional Neural Networks

Jae Hoon Shim;Hochang Rhee;Yeong Il Jang;Geonsu Lee;Seyun Kim;Nam Ik Cho

Year: 2021 | Conference Paper |

Lossless Image Compression by Joint Prediction of Pixel and Context Using Duplex Neural Networks

Hochang Rhee;Yeong Il Jang;Seyun Kim;Nam Ik Cho

IEEE Access
Year: 2021 | Volume: 9 | Journal Article |
Cited by: Papers (9)
This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolution...Show More

Lossless Image Compression by Joint Prediction of Pixel and Context Using Duplex Neural Networks

Hochang Rhee;Yeong Il Jang;Seyun Kim;Nam Ik Cho

Year: 2021 | Volume: 9 | Journal Article |

Channel-Wise Progressive Learning For Lossless Image Compression

Hochang Rhee;Yeong Il Jang;Seyun Kim;Nam Ik Cho

2020 IEEE International Conference on Image Processing (ICIP)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
This paper presents a channel-wise progressive coding system for lossless compression of color images. We follow the classical lossless compression scheme of LOCO-I and CALIC, where pixel values and coding contexts are predicted and forwarded to the entropy coder for compression. The contribution is that we jointly estimate the pixel values and coding contexts from neighboring pixels by training a...Show More

Channel-Wise Progressive Learning For Lossless Image Compression

Hochang Rhee;Yeong Il Jang;Seyun Kim;Nam Ik Cho

Year: 2020 | Conference Paper |

Generalized Product Quantization Network for Semi-Supervised Image Retrieval

Young Kyun Jang;Nam Ik Cho

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (31)
Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning. However, these approaches do not meet expectations unless expensive label information is sufficient. To resolve this issue, we propose the first quantization-based semi-supervised image retrieval scheme: Generalized Product Quantization (GPQ) network. We design a nov...Show More

Generalized Product Quantization Network for Semi-Supervised Image Retrieval

Young Kyun Jang;Nam Ik Cho

Year: 2020 | Conference Paper |

Dual Path Denoising Network for Real Photographic Noise

Yeong Il Jang;Yoonsik Kim;Nam Ik Cho

IEEE Signal Processing Letters
Year: 2020 | Volume: 27 | Journal Article |
Cited by: Papers (8)
This letter presents a convolutional neural network (CNN) for image denoising, especially for the reduction of real noises. As a network topology, we adopt the dual path network (DPN) that combines the advantages of residual and densely connected networks. Using the DPN as a basic building block, we design a network that connects the DPN in dual path again with an attention mechanism. For efficien...Show More

Dual Path Denoising Network for Real Photographic Noise

Yeong Il Jang;Yoonsik Kim;Nam Ik Cho

Year: 2020 | Volume: 27 | Journal Article |

Deep Face Image Retrieval for Cancelable Biometric Authentication

Young Kyun Jang;Nam Ik Cho

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Year: 2019 | Conference Paper |
Cited by: Papers (16)
This paper presents a cancelable biometric system for face authentication by exploiting the convolutional neural network (CNN)-based face image retrieval system. For the cancelable biometrics we must build a template that achieves good performance while maintaining some essential conditions. First the same template should not be used in different applications. Second if the compromise event occurs...Show More

Deep Face Image Retrieval for Cancelable Biometric Authentication

Young Kyun Jang;Nam Ik Cho

Year: 2019 | Conference Paper |

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