Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
EMM |
2024-03-02 14:00 |
Overseas |
Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency |
[Poster Presentation]
Classification of AI generated images by sparse coding Daishi Tanaka, Michiharu Niimi (KIT) EMM2023-89 |
In recent years, advancements in generative AI technologies have made it increasingly challenging for human vision to di... [more] |
EMM2023-89 pp.1-6 |
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 |
NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-24 10:00 |
Tokushima |
Naruto University of Education |
Hierarchical lossless compression of high dynamic range images using predictors based on cellular neural networks Seiya Kushi, Kazuki Nakashima, Hideharu Toda (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Hisashi Aomori (Chukyo Univ.) NLP2023-85 MICT2023-40 MBE2023-31 |
We have been developing a scalable lossless coding method using cellular neural networks (CNN) as predictors. This metho... [more] |
NLP2023-85 MICT2023-40 MBE2023-31 pp.12-15 |
QIT (2nd) |
2023-12-17 17:30 |
Okinawa |
OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Proposal to Reduce Degradation of Quantum Images by Contrast and Edge Enhancement Processing Chihiro Dogo, Kazuhiro Saito (KDDI Research) |
Quantum image processing requires encoding image information expressed in classical 0/1 into a quantum state. There are ... [more] |
|
IE, CS, IPSJ-AVM [detail] |
2023-12-11 16:25 |
Fukuoka |
Kyushu Institute of Technology (Primary: On-site, Secondary: Online) |
[Special Invited Talk]
Considerations on Image Processing, Coding and Sensing Seishi Takamura (Hosei Univ.) CS2023-84 IE2023-26 |
In image/video coding, the input signal may be coded as-is, but image processing, such as noise removal, for example, ma... [more] |
CS2023-84 IE2023-26 p.17 |
CS |
2023-11-09 10:55 |
Shizuoka |
Plaza Verde |
Deep Joint Source-Channel Coding using Overlap Image Division for Block Noise Reduction Ryunosuke Yamamoto, Yoshiaki Inoue, Daisuke Hisano (Osaka Univ.) CS2023-65 |
Deep Joint Source-Channel Coding (Deep JSCC), which uses deep learning to perform source and channel coding simultaneous... [more] |
CS2023-65 pp.16-18 |
SR |
2023-11-10 10:55 |
Miyagi |
(Primary: On-site, Secondary: Online) |
[Short Paper]
On Model Transfer with Deep Joint Source Channel Coding Katsuya Suto, Issa Matsumura, Junichiro Yamada (UEC) SR2023-58 |
Based on the source channel separation theorem, the current multimedia transfer system employs independently designed so... [more] |
SR2023-58 pp.61-63 |
SR |
2023-11-10 11:10 |
Miyagi |
(Primary: On-site, Secondary: Online) |
[Short Paper]
Model Sharing and Learning for Visually Secure Deep Joint Source Channel Coding Yuyang Fu, Katsuya Suto (UEC) SR2023-59 |
Research and development of Deep Joint Source Channel Coding (DeepJSCC) technology, which is a data-driven design of inf... [more] |
SR2023-59 pp.64-66 |
CS |
2023-07-28 14:10 |
Tokyo |
Hachijo-machi Chamber of Commerce and Industry |
Impact of Learning Models on Deep Joint Source Channel Coding Adaptable to 5G Systems Ryunosuke Yamamoto, Keigo Matsumoto, Yoshiaki Inoue (Osaka Univ.), Yuko Hara-Azumi (Tokyo Tech), Kazuki Maruta (TUS), Yu Nakayama (TUAT), Daisuke Hisano (Osaka Univ.) CS2023-56 |
With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (Deep JSCC)... [more] |
CS2023-56 pp.151-156 |
MSS, CAS, SIP, VLD |
2023-07-06 15:20 |
Hokkaido |
(Primary: On-site, Secondary: Online) |
A Study on Golomb Coding for Scrambled Domain Layered Coding Compatible with JPEG XS Takayuki Nakachi (Univ. of the Ryukyus), Hiroyuki Kimiyama (Daido), Mitsuru Murayama (KAIST) CAS2023-10 VLD2023-10 SIP2023-26 MSS2023-10 |
In this report, we propose a Golomb coding method for scrambled domain layered coding compatible with JPEG XS. The propo... [more] |
CAS2023-10 VLD2023-10 SIP2023-26 MSS2023-10 pp.47-52 |
SR |
2023-05-11 15:10 |
Hokkaido |
Center of lifelong learning Kiran (Higashi Muroran) (Primary: On-site, Secondary: Online) |
[Short Paper]
Continuous Compressible Deep Joint Source Channel Coding Junichiro Yamada, Katsuya Suto (UEC) SR2023-11 |
Deep Joint Source Channel Coding (DeepJSCC), which designs a source and channel coding model with deep neural networks a... [more] |
SR2023-11 pp.58-60 |
IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-16 15:15 |
Okinawa |
Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
A Method for Fast Compression of Sign Bits of DCT Coefficients in Image Coding Fuma Ito, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii (Nagoya Univ.) IMQ2022-56 IE2022-133 MVE2022-86 |
Compressing the signs of DCT coefficients is an intractable problem in image coding because of their equiprobable charac... [more] |
IMQ2022-56 IE2022-133 MVE2022-86 pp.178-181 |
SIS |
2023-03-02 14:40 |
Chiba |
Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
QR code image dnoising netwroks based on decodability assessment Kazumitsu Takahashi, Makoto Nakashizuka (CIT) SIS2022-46 |
In this paper, an image denoising method for QR code images is proposed. The image recovery from the degraded QR code im... [more] |
SIS2022-46 pp.33-36 |
SeMI, IPSJ-UBI, IPSJ-MBL |
2023-03-01 17:00 |
Aichi |
(Primary: On-site, Secondary: Online) |
Color Palette Coding: Selective Signal Transmission for CMOS Image Sensor based Communication Tianwen Li, Yu Nakayama (Tokyo Univ. of Agriculture and Tech.) SeMI2022-114 |
Optical camera communication using LEDs and signage as transmitters and CMOS image sensors as receivers is expected to b... [more] |
SeMI2022-114 pp.43-48 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-21 11:10 |
Hokkaido |
Hokkaido Univ. |
[Special Talk]
Study of Probability Modeling for Lossless Image Coding Using Example Search and Adaptive Prediction Hiroki Kojima (KDDI), Yasuyo Kita, Ichiro Matsuda (Tokyo Univ. of Science) ITS2022-46 IE2022-63 |
Many efficient lossless image coding methods predict the next pel value to be coded from the pels already coded, and rem... [more] |
ITS2022-46 IE2022-63 p.25 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-21 15:30 |
Hokkaido |
Hokkaido Univ. |
A Study on Adaptation Methods for Universal Deep Image Compression Koki Tsubota, Kiyoharu Aizawa (UTokyo) ITS2022-56 IE2022-73 |
In this study, we tackle universal deep image compression, which aims to compress images in arbitrary domains such as li... [more] |
ITS2022-56 IE2022-73 pp.77-82 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-22 14:30 |
Hokkaido |
Hokkaido Univ. |
Image Division Based Interframe Prediction Method with Reduced Frame Memory for Ultra-Low-Latency Video-coding Mai Yamaguchi, Matsumura Tetsuya (Nihon Univ.) |
In this paper, we propose a new inter-frame prediction method for Ultra-Low-Latency Video-coding. The proposed method re... [more] |
|
IE |
2023-02-02 15:30 |
Tokyo |
NII (Primary: On-site, Secondary: Online) |
[Invited Talk]
How Can We Compress Signs of DCT Coefficients in Image Coding?
-- A Method Inspired by Phase Retrieval -- Chihiro Tsutake (Nagoya Univ.) IE2022-55 |
Compressing the signs of DCT coefficients is an intractable problem in image coding because of their equiprobable charac... [more] |
IE2022-55 p.19 |
IA |
2023-01-25 13:55 |
Osaka |
Osaka Umeda Campus, Kwansei Gakuin University (Osaka) (Primary: On-site, Secondary: Online) |
Fast Container Image Updating with Binary Delta Encoding Naoki Matsumoto, Daisuke Kotani, Yasuo Okabe (Kyoto Univ.) IA2022-70 |
Container is an execution environment isolation technology for Linux. By distributing container images that consolidate ... [more] |
IA2022-70 pp.14-21 |
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 |