Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-14 11:00 |
Okinawa |
Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
[Invited Talk]
From Pixels to Precision: Passing into the Future of Super-Resolution Mastery Supatta Viriyavisuthisakul (PIM) IMQ2023-42 IE2023-97 MVE2023-71 |
Single Image Super-Resolution (SISR) involves reconstructing low-resolution images to enhance perceptual quality. Recent... [more] |
IMQ2023-42 IE2023-97 MVE2023-71 p.165 |
ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2024-02-20 10:15 |
Hokkaido |
Hokkaido Univ. |
Image Attractiveness Analysis with Explanation using Vision-Language Model Shun Yoshida, Kaede Shiohara, Toshihiko Yamasaki (UTokyo) ITS2023-61 IE2023-50 |
There has been research on making machines analyze the image attractiveness, and in recent years, further progress has b... [more] |
ITS2023-61 IE2023-50 pp.82-87 |
ITS, IE, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2024-02-20 12:45 |
Hokkaido |
Hokkaido Univ. |
3D CG Coded Image Noise Removal and Quality Assessment Based on Optimal Design of Total Variation Regularization Norifumi Kawabata (Kanazawa Gakuin Univ.) ITS2023-67 IE2023-56 |
Sparse coding techniques, which reproduce and represent images with as few combinations as possible from a small amount ... [more] |
ITS2023-67 IE2023-56 pp.112-117 |
LOIS, IPSJ-DC |
2023-08-04 14:45 |
Kyoto |
Kyoto Tachibana University, Keisei-Kan, 1-G106 (Primary: On-site, Secondary: Online) |
Recognizing Human-Centered Contexts for In-Home Elderly Monitoring Using Vision-Based Edge AI Sinan Chen, Masahide Nakamura, Kiyoshi Yasuda (Kobe Univ.) LOIS2023-6 |
As the global population ages, including Japan, there is a significant trend toward transitioning from facility-based ca... [more] |
LOIS2023-6 pp.18-22 |
BioX, SIP, IE, ITE-IST, ITE-ME [detail] |
2023-05-18 15:15 |
Mie |
Sansui Hall, Mie University (Primary: On-site, Secondary: Online) |
SIP2023-5 BioX2023-5 IE2023-5 |
Compressing video and images with lossy compression degrades input data.Therefore, image quality evaluation is necessary... [more] |
SIP2023-5 BioX2023-5 IE2023-5 pp.16-21 |
IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-16 15:40 |
Okinawa |
Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
Iris Image Quality Assessment with Degradation Scores for Recognition Ryuichi Akashi, Yuho Shoji, Takahiro Toizumi, Atsushi Ito (NEC) IMQ2022-57 IE2022-134 MVE2022-87 |
In this paper, we propose an iris image quality assessment method to quantify the influence of each type of image degrad... [more] |
IMQ2022-57 IE2022-134 MVE2022-87 pp.182-187 |
IMQ |
2022-10-21 13:40 |
Aichi |
E and S Building, Higashiyama Campus, Nagoya Univ. |
HEVC Image Quality Assessment for eXtended Reality (XR) Based on 360 Degrees Camera Norifumi Kawabata (Computational Imaging Lab) IMQ2022-12 |
360 degrees video camera is often used in our life, event, information communication service, and Virtual Reality (VR), ... [more] |
IMQ2022-12 pp.7-12 |
SIS, ITE-BCT |
2022-10-14 09:40 |
Aomori |
Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
Creation of subjective evaluation datasets for Print Quality Assessment Ryosuke Tonegawa, Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ) SIS2022-15 |
There does not exist image quality assessment (IQA) dataset for image with print-specific defects so far. This dataset c... [more] |
SIS2022-15 pp.21-26 |
CCS, NLP |
2022-06-09 13:25 |
Osaka |
(Primary: On-site, Secondary: Online) |
Learning Method for Image Denoising by Weighted Sum of Perceptual Quality Assessment Methods Takamichi Miyata (Chiba Inst. Tech.) NLP2022-2 CCS2022-2 |
Existing deep learning-based denoising methods employ mean squared error (MSE) as a loss function. As a result, the outp... [more] |
NLP2022-2 CCS2022-2 pp.7-12 |
EMM |
2022-03-07 15:40 |
Online |
(Primary: Online, Secondary: On-site) (Primary: Online, Secondary: On-site) |
[Poster Presentation]
A Reversible Contrast Enhancement method in HSI Color Space Ayana Wakimizu (Chiba Univ.), Shoko Imaizimi (Chiba univ.) EMM2021-101 |
In this paper, we propose a new reversible image processing method for color images.
While the conventional method enha... [more] |
EMM2021-101 pp.52-57 |
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] |
2022-02-21 12:45 |
Online |
Online |
Quality Assessment for 3D CG Image Colorization Using Visible Digital Watermarking after Noise Removal Based on Sparse Dictionary Learning Coding Norifumi Kawabata (Hokkaido Univ.) |
Thus far, we discussed to represent image data whether it is possible or not to represent meaning image how requirement ... [more] |
|
MI |
2022-01-26 13:00 |
Online |
Online |
Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59 |
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] |
MI2021-59 pp.59-64 |
CQ |
2021-08-04 15:30 |
Online |
Online |
A Factor Analysis of Uncomfortable Feeling caused by Image Retargeting in Retargeted Images using Subjective Assessment and Eye Measurement Yoshikazu Kawayoke (NIT, Ishikawa College), Yasuhiro Inazumi (Yamanashi Eiwa College) CQ2021-34 |
As the resolutions and aspect ratios of displays are becoming increasingly diverse, there is growing interest in image r... [more] |
CQ2021-34 pp.64-69 |
MI |
2021-03-17 11:00 |
Online |
Online |
Optimal Design and Quality Assessment of Color Laparoscopic Super-Resolution Image by Generative Adversarial Networks Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) MI2020-91 |
The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristic... [more] |
MI2020-91 pp.186-190 |
CQ, CBE (Joint) |
2020-01-17 13:40 |
Tokyo |
NHK Science & Technology Research Laboratories |
A Method of Glossiness Preserving Image Coding Tomoyuki Takanashi, Midori Tanaka, Takahiko Horiuchi (Chiba Univ.) CQ2019-130 |
Image coding plays an important role to reduce their cost for storage or transmission. Current image coding techniques u... [more] |
CQ2019-130 pp.131-136 |
IMQ |
2019-10-04 14:00 |
Osaka |
Osaka University |
3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding Norifumi Kawabata (Tokyo Univ. of Science) IMQ2019-6 |
By appearing of high-definition and high-quality images, it comes to increase many chance to process image big data. If ... [more] |
IMQ2019-6 pp.1-10 |
CQ |
2019-07-18 15:50 |
Niigata |
Niigata Univ. |
[Special Invited Talk]
An overview of Ultra-reality communication technologies and expectations to its future QoE assessment
-- Spatial value creation by ultra-reality -- Eisaburo Itakura (Sony IP&S) CQ2019-51 |
By recent advances of ICT, ultra-communication systems, which have reality qualitatively different from conventional aud... [more] |
CQ2019-51 pp.73-77 |
IE |
2019-06-21 14:30 |
Okinawa |
|
Image Quality Evaluation of 8K120Hz HEVC Encoder Shinya Iwasaki (NHK), Xuying Lei (FJL), Kazuhiro Chida, Yasuko Sugito, Kazuhisa Iguchi, Kikufumi Kanda (NHK), Hidenobu Miyoshi, Yoshifumi Uehara (FJL) IE2019-20 |
We developed 8K 119.88-Hz (120-Hz) encoder to transmit videos with smoother motions. To evaluate the image quality impro... [more] |
IE2019-20 pp.19-24 |
SIP |
2018-08-20 16:00 |
Tokyo |
Takushoku Univ. Bunkyo Campus. |
[Invited Talk]
Image and Video Inpainting
-- Recent Advances in Inpainting and its Subjective Quality Assessment -- Mariko Isogawa (NTT) SIP2018-61 |
(To be available after the conference date) [more] |
SIP2018-61 p.25 |
IMQ, HIP |
2018-07-20 13:30 |
Hokkaido |
Sapporo City University, Satellite Campus |
[Invited Lecture]
Inpainting based on low-dimensional image approximation and its applications Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) IMQ2018-5 HIP2018-32 |
This paper introduces inpainting based on low-dimensional image approximation and its applications. Specifically, low-di... [more] |
IMQ2018-5 HIP2018-32 pp.1-4 |