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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] | 2022-11-30 16:15 |
Kumamoto | (Primary: On-site, Secondary: Online) |
FPGA Implementation of Learned Image Compression Heming Sun (Waseda U), Qingyang Yi (UTokyo), Jiro Katto (Waseda U), Masahiro Fujita (UTokyo)  VLD2022-53 ICD2022-70 DC2022-69 RECONF2022-76 |
Learned image compression (LIC) has reached a superior coding gain than traditional hand-crafted standards such as JPEG ... [more] | VLD2022-53 ICD2022-70 DC2022-69 RECONF2022-76 pp.194-199 |
| 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 |
| CAS, NLP | 2022-10-20 14:55 |
Niigata | (Primary: On-site, Secondary: Online) |
Hierarchical Lossless Coding with Arithmetic Coders for Each CNN Predictor Kazuki Nakashima, Ryo Nakazawa, Hideharu Toda, Hisashi Aomori (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Ichiro Matsuda, Susumu Itoh (TUS) CAS2022-23 NLP2022-43 |
We have been developing a scalable lossless coding method using the cellular neural networks (CNN) as predictors. This ... [more] |
CAS2022-23 NLP2022-43 pp.20-24 |
| SIS, ITE-BCT | 2022-10-13 13:55 |
Aomori | Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
An 8K image perceptual scramble scheme for JPEG XS standard Takayuki Nakachi (Univ. of the Ryukyus), Hiroyuki Kimiyama (Daido), Mitsuru Murayama (KAIST) SIS2022-11 |
In this report, we propose a scrambled image compression algorithm for the JPEG XS standard. The JPEG XS is a low-delay,... [more] | SIS2022-11 pp.1-6 |
| R | 2022-07-29 13:55 |
Hokkaido | (Primary: On-site, Secondary: Online) |
A Comparison Study on Image Captioning by VGG and YOLO Yan LYU, Qiangfu Zhao, Yong Liu (UoA) R2022-10 |
Image captioning is a task for generating a descriptive statement automatically for a given image by combining image pro... [more] | R2022-10 pp.7-12 |
| IT, EMM | 2022-05-17 13:25 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
A Note on Time-Varying Two-Dimensional Autoregressive Models and the Bayes Codes Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2022-2 EMM2022-2 |
This paper proposes a two-dimensional autoregressive model with time-varying parameters as a stochastic model for explai... [more] | IT2022-2 EMM2022-2 pp.7-12 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-10 15:00 |
Online | Online (Zoom) | [Special Talk]
Lossless Image Coding using Inpainting-Oriented Deep Pixel Predictor Keita Takahashi (Nagoya Univ.) IMQ2021-31 CQ2021-122 IE2021-93 MVE2021-60 |
I will be presenting our previous paper that received IE special Award 2020 to encourage discussions for future directio... [more] | IMQ2021-31 CQ2021-122 IE2021-93 MVE2021-60 p.114(IMQ), p.124(CQ), p.114(IE), p.114(MVE) |
| 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] | |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-21 10:55 |
Online | Online | Recognition Using YOLOv5 for Degraded Images on Image Sensor Communication Hiroko Matsuda, Haruna Matsushita (Kagawa Univ), Shintaro Arai (Okayama Univ of Sci.) NLP2021-77 MICT2021-52 MBE2021-38 |
This paper focuses on the image sensor communication (ISC) and propose a signal demodulation method using You Only Loo... [more] |
NLP2021-77 MICT2021-52 MBE2021-38 pp.31-34 |
| IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2021-06-03 16:00 |
Online | Online | Fast Implementation of the Lossless Image Coding Method Based on Example Search and Probability Model Optimization Hiroki Kojima, Yusuke Kameda, Yasuyo Kita, Ichiro Matsuda, Susumu Itoh (Tokyo Univ of Science.) SIP2021-3 BioX2021-3 IE2021-3 |
We previously proposed a lossless image coding method based on example search and probability model optimization. In the... [more] | SIP2021-3 BioX2021-3 IE2021-3 pp.10-14 |
| WBS, IT, ISEC | 2021-03-04 13:20 |
Online | Online | [Poster Presentation]
Experimental Evaluation for Alamouti-type Spatio-temporal Coding in Image Sensor Communication Using a Rotary LED Transmitter Zhengqiang Tang, Shintaro Arai (Okayama Univ. of Sci.), Takaya Yamazato (Nagoya Univ.) IT2020-125 ISEC2020-55 WBS2020-44 |
This study provides the experimental evaluations for afterimage-based Alamouti-type Spatio-temporal coding (STC) in imag... [more] | IT2020-125 ISEC2020-55 WBS2020-44 pp.86-91 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2021-02-19 14:05 |
Online | Online | [Special Talk]
A Note on Discrimination of Road Surface Conditions Based on Deep Learning Using Road Images Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study the discrimination of road surface conditions based on deep learning using images captured by fi... [more] | |
| IE | 2021-01-21 13:00 |
Online | Online | Comparing Pixel Predictors with Different Coding Order for Lossless Image Coding Aki Kunieda, Keita Takahashi, Toshiaki Fujii (Nagoya Univ.) IE2020-34 |
The efficiency of lossless image coding depends on the pixel predictors, with which unknown pixels are predicted from al... [more] | IE2020-34 pp.1-6 |
| IE | 2021-01-21 14:45 |
Online | Online | [Invited Talk]
GAN-based Image Coding Methods for Maximizing Subjective Image Quality Shinobu Kudo (NTT) IE2020-37 |
The increasing image resolution and the spread of IoT devices require more efficient video storage and transmission syst... [more] | IE2020-37 pp.9-13 |
| NC, NLP (Joint) |
2021-01-22 09:40 |
Online | Online | On decoding left-right movement intention of single arm from EEG Mitsuhiko Inaba, Kazushi Ikeda (NAIST), Motoaki Kawanabe (ATR) NC2020-35 |
Brain-machine interface (BMI) is a technology that supports people by manipulating external devices using only changes i... [more] | NC2020-35 pp.18-23 |
| SIP, IT, RCS | 2021-01-22 15:15 |
Online | Online | An Image Generative Model with Various Auto-regressive Coefficients Depending on Neighboring Pixels and the Bayes Code for It Masahiro Takano, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-108 SIP2020-86 RCS2020-199 |
In this papar, we propose an expanded model of an autoregressive stochastic generative model for images. This model cont... [more] | IT2020-108 SIP2020-86 RCS2020-199 pp.253-258 |
| MI | 2020-09-03 13:10 |
Online | Online | [Invited Talk]
Manifold modeling in embedded space for image restoration Tatsuya Yokota (Nitech) MI2020-27 |
In this invited talk, I will discuss convolutional neural networks, which have achieved remarkable results in various im... [more] | MI2020-27 pp.43-44 |
| IT, EMM | 2020-05-28 15:25 |
Online | Online | An Autoregressive Image Generative Model and the Bayes Code for It Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-4 EMM2020-4 |
In this paper, we propose an autoregressive stochastic generative model for images. This model should be one of the mos... [more] |
IT2020-4 EMM2020-4 pp.19-24 |
| PRMU, IPSJ-CVIM | 2020-03-16 16:45 |
Kyoto | (Cancelled but technical report was issued) |
Image compression by colorization Hiya Roy, Subhajit Chaudhury, Toshihiko Yamasaki, Tatsuaki Hashimoto (UTokyo) PRMU2019-86 |
Image compression techniques exploit the inherent psycho-visual limitations in human vision to reduce the number of bits... [more] | PRMU2019-86 pp.107-108 |
| IE, IMQ, MVE, CQ (Joint) [detail] |
2020-03-06 14:50 |
Fukuoka | Kyushu Institute of Technology (Cancelled but technical report was issued) |
A high-compression video coding method for video analysis using Deep Learning Tomonori Kubota, Takanori Nakao, Eiji Yoshida (Fujitsu Lab.) IMQ2019-39 IE2019-121 MVE2019-60 |
In this paper, we propose a high-compression video coding method for video analysis using Deep Learning. The method anal... [more] | IMQ2019-39 IE2019-121 MVE2019-60 pp.121-126 |
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