IEEE JOURNAL ON

EMERGING AND SELECTED TOPICS
IN CIRCUITS AND SYSTEMSCALL for PAPERS
Advances in Generative Visual Signal Coding and Processing
Guest Editors
Zhibo Chen, University of Science and Technology of China, China (chenzhibo@ustc.edu.cn)
Heming Sun, Yokohama National University, Japan (sun-heming-vg@ynu.ac.jp)
Li Zhang, Bytedance Inc., US (lizhang.idm@bytedance.com)
Fan Zhang, University of Bristol, UK (fan.zhang@bristol.ac.uk)

Scope and Purpose
In recent years, generative models have emerged as one of the most significant and rapidly developing areas of researchin artificial intelligence. Generative models have demonstrated remarkable success in synthesizing high-quality data
(image, video, text, etc.) and hold the promise for utilizing unlabeled data, transfer learning, and many otherapplications. Moreover, it has also been proven that generative models are an important instrument for advancing
research in AI-based visual signal coding and processing. For instance, variational autoencoders (VAE) has been used asa fundamental framework in end-to-end learning-based image coding schemes, autoregressive (AR) model has been
extensively studied to improve entropy coding performance, and generative adversarial networks (GANs) has beenutilized frequently to enhance the subjective quality of coding schemes. Meanwhile, generative models have also been
explored in various visual signal processing tasks, including visual signal quality assessment, restoration, enhancement,editing, and interpolation.
In light of the rapid growth of generative visual signal coding and processing, its contributions to international standards
and practical application optimization will be increasingly valued. The Joint Video Experts Team (JVET) of ITU-T VCEG andISO/IEC MPEG has started working together on an exploration study to evaluate potential neural network-based videocoding (NNVC) technology to surpass conventional hybrid video coding frameworks. Additionally, MPEG also launchedmany standardization projects which have started adopting AI-based technologies, such as AI-based 3D graphics coding,AI model compression and video coding for machines (VCM). Meanwhile, JPEG AI, the creation of a learning-based image
coding standard, a joint standardization project between IEC, ISO and ITU, is under development.
In addition to performance gains, a key challenge with generative neural network models is their substantial
computational and memory demands. Regarding the hardware and implementation, most frameworks are accelerated
by the general-purpose accelerators. However, the hardware utilization of GPU/DPU is relatively low since thecomputation-to-communication ratio of a developed neural network may not fit the hardware resources such as thenumber of PE cores and bandwidth. To cater for the rapid update of neural network architectures, developing a generaland efficient hardware accelerator is also challenging. Besides, to meet the practicality, some other issues such as privacy
and security in generative visual signal coding and processing also need to be addressed.
The intent of this special issue is to present the latest developments in algorithms, implementations, and applications
related to generative visual signal coding and processing technologies. Original and unpublished research results with
topics in any of the following areas or beyond are hereby solicited.

Topics of Interest•••••

Generative visual signal (image, video, 3D, light field, point-cloud) coding
Generative visual signal coding for machine vision applications
Coding and transmission of AI generated content
Quality assessment of AI generated content
Visual quality assessment for generative visual signal coding and processingIEEE JOURNAL ON

EMERGING AND SELECTED TOPICS
IN CIRCUITS AND SYSTEMS•••••••

Circuits and systems for generative visual signal coding and processing
FPGA/ASIC implementation for generative model
Complexity reduction for generative networks
Generative visual data restoration and enhancement
Compossible system for AI generated content
Interactive content generation and editing
3D content generation and editing

Submission Procedure
Prospective authors are invited to submit their papers following the instructions provided on the JETCAS web-site:
https://ieee.atyponrex.com/journal/jetcas. The submitted manuscripts should not have been previously published norshould they be currently under consideration for publication elsewhere.

Important Dates•••••

Manuscript submissions due
First round of reviews completed
Revised manuscripts due
Second round of reviews completed
Final manuscripts due

Dec. 15, 2023
Jan. 29, 2024
Mar. 04, 2024
Mar. 31, 2024
Apr. 15, 2024

Request for Information
Corresponding Guest Editor: Zhibo Chen, University of Science and Technology of China, China (chenzhibo@ustc.edu.cn)