Workshop Title:

Semantic Communication for Media Compression and Transmission

Date:

August 22th, 2025 (UTC+1)

Organizer:

Department of Computer and Information Sciences, University of Strathclyde

Keywords:

• Image Transmission
• Video Transmission
• Semantic Communication
• Task-oriented Communication

Workshop Chair:

Personal Bio:

Prof. Anil Fernando received the B.Sc. (Hons.) degree (First Class) in electronics and telecommunication engineering from the University of Moratuwa, Sri Lanka, in 1995, and the M.Sc. in Communications (Distinction) from the Asian Institute of Technology, Bangkok, Thailand in 1997 and Ph.D. in Computer Science (Video Coding and Communications) from the University of Bristol, UK in 2001. He is a professor in Video Coding and Communications at the Department of Computer and Information Sciences, University of Strathclyde, UK. He leads the video coding and communication research team at Strathclyde. He has worked on major national and international multidisciplinary research projects and led most of them. He has published over 400 papers in international journals and conference proceedings and published a book on 3D video broadcasting. He has been working with all major EU broadcasters, BBC, and major European media companies/SMEs in the last decade in providing innovative media technologies for British and EU citizens. His main research interests are in Video coding and Communications, Machine Learning (ML) and Artificial Intelligence (AI), Semantic Communications, Signal Processing, Networking and Communications, Interactive Systems, Resource Optimizations in 6G, Distributed Technologies, Media Broadcasting and Quality of Experience (QoE).

Workshop Committee Members:

Mr. Prabhath Samarathunga, Researcher Prabhath.Samarathunga@strath.ac.uk

Mrs. Udara Jayasinghe, Researcher udara.jayasinghe-mudalige@strath.ac.u

Background:

Semantic communication, a concept first discussed by Shannon and Weaver in 1949, classifies communication challenges into three distinct levels: physical, semantic, and effectiveness. The physical problem concerns the accurate and reliable transmission of the raw data content of a message, which led to the development of information theory—a field that has profoundly influenced modern communication technologies. The semantic problem, in contrast, deals with ensuring that the intended meaning or context of a message is accurately delivered to the receiver. Finally, the effectiveness problem focuses on determining whether the message achieves its intended purpose or prompts the desired action from the recipient. While advancements in physical communications have progressed exponentially since the early days of information theory, laying the groundwork for today’s high-performance gaming, entertainment, and media ecosystems, semantic communication has remained underexplored for decades. This stagnation can largely be attributed to the absence of computational and theoretical tools required to implement semantic communication systems effectively.Recent advancements in deep learning, natural language processing (NLP), and computational performance have made it possible to revisit semantic communication as a practical and transformative paradigm. Unlike traditional communication methods that prioritize transmitting raw data with high fidelity, semantic communication focuses on delivering meaning, intent, or relevance while minimizing unnecessary data redundancy. This shift is particularly relevant for addressing modern challenges, such as the growing demand for bandwidth-intensive applications, low-latency connectivity, and efficient energy use in data transmission. Semantic communication enables intelligent and context-aware transmission, making it a promising solution to improve the capacity, scalability, and reliability of current and future communication systems. In summary, semantic communication is revolutionizing media compression and transmission by emphasizing meaning over raw data. This paradigm aligns well with the challenges of next-generation networks, such as 5G, 6G, and IoT, which require solutions for bandwidth optimization, latency reduction, and scalability. Its applications span a wide range of fields, including entertainment, gaming, smart devices, and autonomous systems, making it a critical component of future communication systems.

Goal/Rationale:

In conventional transmission systems, data transmission occurs at the bit level, where the source—whether it be text, audio, images, or video—is converted into a sequence of bits for transmission. At the receiver’s end, this bit sequence is decoded to recover the original data. A variety of compression techniques have been developed to efficiently transmit data over limited bandwidth channels. However, all existing compression and transmission methods operate within the constraints of Shannon capacity, which defines the theoretical maximum data rate that can be transmitted over a communication channel without error. These techniques aim to optimize the transmission of data by minimizing distortion, but they still rely on bit-level representations that may not be the most efficient for specific tasks.Traditional bit-level communication systems focus primarily on minimizing distortion during data transmission over physical channels. However, when the goal is to recover data for specific tasks—such as classification, object detection, image segmentation, or similar applications—the bit-level recovery process often results in poor performance. This is because the distortion introduced by the compression techniques affects the task's effectiveness at the receiver's end. Moreover, in bandwidth-constrained environments such as mobile communication channels, conventional bit-level compression techniques have reached their limits in optimizing rate-distortion trade-offs. This is especially problematic when handling the growing demands of real-time, high-resolution multimedia content, such as high-definition video streaming and interactive gaming.

As the scope of data communication has evolved, a significant portion of content—such as text, audio, images, and video—is increasingly analyzed and processed by machines rather than humans. This shift makes it critical to optimize transmission and compression techniques for machine-to-machine (M2M) and machine-to-human (M2H) communication systems. With the proliferation of video-based communications in sectors like autonomous driving, smart factories, gaming, IoT, and remote sensing, there is a growing strain on the already bandwidth-constrained channels. These applications, particularly those involving real-time interactions with high-resolution video, present a significant challenge for conventional communication systems, which are not designed to efficiently handle such high-demand scenarios. While existing systems may manage these tasks in controlled environments, they are unable to meet the needs of modern Video IoT (VIoT) applications in fields like digital manufacturing or autonomous vehicles, where real-time, high-resolution video transmission between devices and humans is critical.

Scope and Information for Participants:

Learning semantic communication concepts is becoming increasingly important for a range of professionals and researchers in various fields. Professionals and researchers across multiple fields need to learn semantic communication concepts to stay at the forefront of technological advancements. These include engineers and developers in telecommunications, media compression, and streaming technologies, who are optimizing bandwidth and enhancing efficiency. AI and machine learning experts, particularly in areas like natural language processing and personalized content delivery, rely on semantic understanding for better decision-making systems. VioT/IoT developers and smart city planners use these concepts to transmit meaningful data in resource-constrained environments. Additionally, cybersecurity professionals must understand semantic communication to ensure secure and privacy-preserving data transmission. Scholars, data scientists, and policymakers also benefit from grasping these ideas to drive innovation, set standards, and regulate emerging technologies.

This workshop will explore how semantic communication can address these emerging challenges in media communications. We will examine how semantic communication can reduce the strain on bandwidth by transmitting only the relevant features needed for specific tasks, thereby optimizing network resources. Additionally, the tutorial will discuss the potential challenges of implementing semantic communication systems and outline strategies to overcome these obstacles. Beyond the communication between people and machines, we will also focus on communication between devices, processes, and objects, highlighting how semantic communication can support the growing complexity of machine-to-machine and device-to-device communication in next-generation networks.

Venue:

Room LT507, Livingston Tower, Department of Computer and Information Sciences,

University of Strathclyde, Glasgow, UK

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