[HIRING!] 2026 Graduate Students & Undergraduate Interns (석박사 대학원생 및 학부인턴 모집)
Juyeon Seo*, Jiseung Yoo*, HyeJi Jang, and Hyunsuk Ko
This dataset consists of NR-domain images obtained by applying DRUNet, NNPF, and the proposed network to each VTM-decoded hologram sequence, and corresponds to the dataset used for subjective quality assessment in "ROI-guided Dual-domain Network for Enhanced Numerical Reconstruction of Compressed Holograms".
Please refer to the link below:
Digital hologram compression introduces distortions that become perceptually visible after numerical reconstruction (NR). Evaluating quality directly in the NR spatial domain is therefore essential for understanding perceptual degradation.
The SOQ-NRC dataset provides NR-domain images generated from VTM-compressed holograms. Each decoded hologram sequence is processed using DRUNet, NNPF, and the proposed network to produce NR images with diverse distortion levels. Multiple QP settings are selected to cover a range of compression severities.
By providing NR images along with corresponding Mean Opinion Scores (MOS), SOQ-NRC serves as a benchmark dataset for subjective quality assessment, restoration comparison, and perceptual analysis of compression artifacts in holographic imaging systems.
The SOQ-NRC dataset provides NR-domain images generated from VTM-compressed holograms.
For each hologram sequence, multiple compression levels are selected using representative quantization parameters (QPs).
The decoded holograms are filtered using DRUNet, NNPF, and the proposed network, and the resulting numerically reconstructed images are included for subjective quality assessment.
We provide processed versions of the holographic images supplied by B<>com. The specifications of the original holograms are summarized below.
Repository: B<>com Hologram Repository
Reference Papers:
A. Gilles, P. Gioia, R. Cozot, and L. Morin, "Hybrid approach for fast occlusion processing in computer-generated hologram calculation," Appl. Opt., AO, vol. 55, no. 20, pp. 5459-5470, Jul. 2016.
A. Gilles, P. Gioia, R. Cozot, and L. Morin, "Computer generated hologram from Multiview-plus-Depth data considering specular reflections," in 2016 IEEE International Conference on Multimedia Expo Workshops (ICMEW), 2016, pp. 1-6.
Numerical reconstruction (NR) is performed with the Numerical Reconstruction Software for Holography (NRSH) version 16.0.
This dataset and resources are provided by the Intelligent Visual Media Laboratory (IVML) and are intended for research and academic use only. By using this dataset, you agree to cite our paper "ROI-guided Dual-domain Network for Enhanced Numerical Reconstruction of Compressed Holograms" in any publications or presentations that utilize this dataset.
Copyright (c) 2026 Intelligent Visual Media Laboratory (IVML)
All rights reserved.
The VTM-FCH dataset is copyrighted by the Intelligent Visual Media Laboratory (IVML). All rights are reserved. Unauthorized use, reproduction, or distribution of this dataset, or any portion thereof, is strictly prohibited without prior written permission from IVML. For any inquiries regarding the dataset, please contact us at hyeseo@hanyang.ac.kr. Permission is not granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this dataset and its documentation for any purpose.