First IEEE Workshop on Coding for Machines

July 10, 2023, Brisbane, Australia

in conjunction with IEEE ICME 2023 

Call for Papers

Multimedia signals – speech, audio, images, video, point clouds, light fields, … – have traditionally been acquired, processed, and compressed for human use. However, it is estimated that in the near future, the majority of Internet connections will be machine-to-machine (M2M). So, increasingly, the data communicated across networks is primarily intended for automated machine analysis. Applications include remote monitoring, surveillance, and diagnostics, autonomous driving and navigation, smart homes / buildings / neighborhoods / cities, and so on. This necessitates rethinking of traditional compression and pre-/post-processing methods to facilitate efficient machine-based analysis of multimedia signals. As a result, standardization efforts such as MPEG VCM (Video Coding for Machines) and JPEG AI have been launched.

Both the theory and early design examples have shown that significant bit savings for a given inference accuracy are possible compared to traditional human-oriented coding approaches. However, a number of open issues remain. These include a thorough understanding of the tradeoffs involved in coding for machines, coding for multiple machine tasks, as well as combined human-machine use, model architectures, software and hardware optimization, error resilience, privacy, security, and others. The proposed workshop is intended to bring together researchers from academia, industry, and government who are working on related problems, provide a snapshot of the current research and standardization efforts in the area, and generate ideas for future work. We welcome papers on the following and related topics:

Important dates

Paper submission: 30-Mar-23
(See submission instructions below)

Acceptance notification: 24-Apr-23

Camera-ready papers: 1-May-23

Submission instructions

ICME and its workshops are in-person events. Please read the Presentation guarantee section at


Ying Liu, Santa Clara University, USA

Heming Sun, Waseda University, Japan

Hyomin Choi, InterDigital, USA

Fengqing Maggie Zhu, Purdue University, USA

Jiangtao Wen, Tsinghua University, China

Ivan V. Bajić, Simon Fraser University, Canada

Technical Program Committee

Balu Adsumilli, Google/YouTube, USA

Nilesh Ahuja, Intel Labs, USA

João Ascenso, Instituto Superior Técnico, Portugal

Zhihao Duan, Purdue University, USA

Yuxing (Erica) Han, Tsinghua University, China

Wei Jiang, Futurewei, USA

Hari Kalva, Florida Atlantic University, USA

André Kaup, Friedrich-Alexander University Erlangen-Nuremberg, Germany

Xiang Li, Google, USA

Weisi Lin, Nanyang Technological University, Singapore

Jiaying Liu, Peking University, China

Ambarish Natu, Australian Government

Saeed Ranjbar Alvar, Huawei, Canada

Donggyu Sim, Kwangwoon University, Korea

Shiqi Wang, City University of Hong Kong

Li Zhang, ByteDance, USA