Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery

TL;DR

This paper proposes a unified semantic loss model for Deep JSCC transmission of Earth Observation imagery, balancing image quality and task performance under resource constraints.

Contribution

It introduces a comprehensive semantic loss framework that integrates reconstruction and task-oriented metrics for EO image transmission using Deep JSCC.

Findings

Unified semantic loss captures the relationship between compression, SNR, and semantic quality.

The framework improves robustness and efficiency in satellite EO imagery transmission.

Empirical analysis validates the effectiveness of the proposed model.

Abstract

Modern Earth Observation (EO) systems increasingly rely on high-resolution imagery to support critical applications such as environmental monitoring, disaster response, and land-use analysis. Although these applications benefit from detailed visual data, the resulting data volumes impose significant challenges on satellite communication systems constrained by limited bandwidth, power, and dynamic link conditions. To address these limitations, this paper investigates Deep Joint Source-Channel Coding (DJSCC) as an effective source-channel paradigm for the transmission of EO imagery. We focus on two complementary aspects of semantic loss in DJSCC-based systems. First, a reconstruction-centric framework is evaluated by analyzing the semantic degradation of reconstructed images under varying compression ratios and channel signal-to-noise ratios (SNR). Second, a task-oriented framework is developed by integrating DJSCC with lightweight, application-specific models (e.g., EfficientViT), with performance measured using downstream task accuracy rather than pixel-level fidelity. Based on extensive empirical analysis, we propose a unified semantic loss framework that captures both reconstruction-centric and task-oriented performance within a single model. This framework characterizes the implicit relationship between JSCC compression, channel SNR, and semantic quality, offering actionable insights for the design of robust and efficient EO imagery transmission under resource-constrained satellite links.