Recent advances in deep learning-based joint source-channel coding (deepJSCC) have substantially improved communication performance, but their high computational cost hinders practical deployment. Moreover, certain applications require the ability to dynamically adapt computational complexity. To address these issues, we propose a Feature Importance-Aware deepJSCC (FAJSCC) model for image transmission that is both computationally efficient and adjustable. FAJSCC employs axis-dimension specialized computation, which performs efficient operations individually for each spatial and channel axis, significantly reducing computational cost while representing features effectively. It further incorporates selective deformable self-attention, which applies self-attention only to selected and adaptively adjusted features, leveraging the importance and relations of input features to efficiently capture complex feature correlations. Another key feature of FAJSCC is that the number of selected important areas can be controlled separately by the encoder and the decoder, depending on the available computational budget. It makes FAJSCC the first deepJSCC architecture to allow independent adjustment of encoder and decoder complexity within a single trained model. Experimental results show that FAJSCC achieves superior image transmission performance under various channel conditions while requiring less computational complexity than recent state-of-the-art models. Furthermore, experiments independently varying the encoder and decoder's computational resources reveal, for the first time in the deepJSCC literature, that understanding the meaning of noisy features in the decoder demands the greatest computational cost. The code is publicly available at github.com/hansung-choi/FAJSCCv2.

翻译：近年来，基于深度学习的联合信源信道编码（deepJSCC）取得了显著进展，大幅提升了通信性能，但其高计算成本阻碍了实际部署。此外，某些应用需要能够动态适应计算复杂度。为解决这些问题，我们提出了一种用于图像传输的特征重要性感知深度联合信源信道编码（FAJSCC）模型，该模型兼具计算高效性和可调节性。FAJSCC采用轴维度专业化计算，该技术针对每个空间轴和通道轴分别执行高效运算，在有效表示特征的同时显著降低了计算成本。它进一步结合了选择性可变形自注意力机制，该机制仅对经过选择和自适应调整的特征应用自注意力，利用输入特征的重要性和相互关系来高效捕获复杂的特征关联。FAJSCC的另一个关键特性是，所选重要区域的数量可以由编码器和解码器根据可用的计算预算分别控制。这使得FAJSCC成为首个允许在单个训练模型内独立调节编码器和解码器复杂度的deepJSCC架构。实验结果表明，在各种信道条件下，FAJSCC在实现优异图像传输性能的同时，其所需的计算复杂度低于当前最先进的模型。此外，通过独立改变编码器和解码器计算资源的实验首次在deepJSCC文献中揭示：在解码器中理解含噪特征的含义需要最大的计算成本。代码已在 github.com/hansung-choi/FAJSCCv2 公开。