A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications

2026-01-29

AI Summary

This work addresses key challenges in visual semantic communication, including imprecise semantic quantization, weak reconstruction robustness, difficulty in transmitter–receiver coordination, and poor channel adaptability. It presents a systematic survey of SemCom-Vision, bridging computer vision and communication engineering, and introduces for the first time a unified taxonomy based on three communication paradigms: semantic preservation, expansion, and refinement. Building upon this framework, the study integrates machine learning–driven codec architectures, semantic quantization mechanisms, knowledge structure modeling, and cross-domain collaborative optimization to establish an interdisciplinary design guideline. By clarifying the applicability of various approaches across different scenarios, this work advances semantic communication from theoretical exploration toward practical deployment, offering both foundational insights and a technical roadmap for future systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Multiagent Systems: Agent Communication
• Machine Learning: Multimodal Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Semantic communication (SemCom) emerges as a transformative paradigm for traffic-intensive visual data transmission, shifting focus from raw data to meaningful content transmission and relieving the increasing pressure on communication resources. However, to achieve SemCom, challenges are faced in accurate semantic quantization for visual data, robust semantic extraction and reconstruction under diverse tasks and goals, transceiver coordination with effective knowledge utilization, and adaptation to unpredictable wireless communication environments. In this paper, we present a systematic review of SemCom for visual data transmission (SemCom-Vision), wherein an interdisciplinary analysis integrating computer vision (CV) and communication engineering is conducted to provide comprehensive guidelines for the machine learning (ML)-empowered SemCom-Vision design. Specifically, this survey first elucidates the basics and key concepts of SemCom. Then, we introduce a novel classification perspective to categorize existing SemCom-Vision approaches as semantic preservation communication (SPC), semantic expansion communication (SEC), and semantic refinement communication (SRC) based on communication goals interpreted through semantic quantization schemes. Moreover, this survey articulates the ML-based encoder-decoder models and training algorithms for each SemCom-Vision category, followed by knowledge structure and utilization strategies. Finally, we discuss potential SemCom-Vision applications.

Problem

• Semantic Communication
• Visual Data Transmission
• Semantic Quantization
• Wireless Communication
• Robust Semantic Extraction

Innovation

• Semantic Communication
• Vision
• Semantic Quantization
• Machine Learning
• Encoder-Decoder Models