# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 1 Introduction
### SemCom-Enabled Satellite Communications  
To achieve stable satellite-to-ground image transmission, scholars have conducted extensive research on image restoration. In [9], the dark channel prior method is employed to enhance the visual clarity of satellite photographs and reduce haze, particularly in poor weather conditions. [PERSON] et al. in [10], utilized techniques such as multi-band digital image color synthesis and multi-spectral image band combination to further improve the clarity of satellite images. Additionally, the study presented in [11] applies a recursive least squares (RLS) adaptive filter to enhance the recovery performance of retrieved satellite images. However, these methods require a large amount of real-time computing, which puts forward huge requirements for satellite computing power and cannot meet the growing demand for data transmission.  
In this scenario, the burgeoning Semantic Communication (SemCom) technology has increasingly come into focus. The SemCom system leverages logical reasoning capabilities to grasp the inherent meaning of information [12], specifically the intent of the message, and maps the signal space onto the semantic space. This approach significantly diminishes the volume of business transmission and substantially enhances transmission efficiency [13]. SemCom goes beyond mere data transmission, placing equal emphasis on the semantic information underlying the data [14]. By extracting and utilizing this semantic information, it facilitates more efficient transmission within limited bandwidth constraints [15]. Consequently, SemCom is exceptionally well-suited for the extensive multimedia data transmissions involved in satellite communications, including images and videos [16].  
Recent studies have explored the application of SemCom in satellite-ground scenarios from various perspectives. Regarding frequency band selection, [PERSON] et al. [17] proposed a multi-agent reinforcement learning method that significantly enhances classification accuracy. For energy efficiency optimization, the integration of a privacy-preserving task-offloading algorithm [18] and an ML-based semantic encoder [19] has achieved a balance between task completion time, energy consumption, and the quality of service. Furthermore, the scope and efficiency of task processing have been enhanced through the implementation of a non-ground network architecture [20] and a task-offloading framework [21].  
In the realm of routing optimization, [PERSON] et al. [22] developed a SemCom-aware routing scheme to improve data throughput and end-to-end delay performance. Another study [23] enhanced transmission efficiency by leveraging time-varying channel conditions and specific content through a routing-based semantic adaptive coding and hybrid automatic repeat request (HARQ) mechanism. Additionally, the semantic enhancement system uses orthogonal time-frequency space (OTFS) modulation and generative adversarial network (GAN) to extract and recover semantic features, which significantly improves signal-to-noise ratio (SNR) gain and spectral efficiency [24].  
For information perception, an adaptive semantic encoder-decoder architecture focuses on utilizing channel conditions and content correlation to protect and restore specific functions [25]. The perception-communication-computing-actuation-integrated paradigm (PCCAIP), as proposed in [26], optimizes communication process through the integration of sensing, computing, and actuation technologies. In terms of delay optimization, papers [24; 25; 27] have improved transmission efficiency by integrating SemCom with different modulation methods.