Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission

• URL: http://arxiv.org/abs/2604.22338v1
• Date: Fri, 24 Apr 2026 08:12:41 GMT
• Title: Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission
• Abstract summary: We propose a lightweight joint source-channel coding (JSCC) framework that incorporates a selective replacement strategy.<n>By adjusting the proportion of layers replaced, we achieve different model compression levels and analyze their impact on reconstruction performance.<n>Our results show that Conv-to-DSConv replacement at intermediate layers achieves a favorable complexity-performance trade-off.
• Score: 49.01183875724542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depthwise separable convolutional (DSConv) layers have been successfully applied to deep learning (DL)-based joint source-channel coding (JSCC) schemes to reduce computational complexity. However, a systematic investigation of the layerwise and ratio-wise replacement of standard convolutional (Conv) layers with DSConv layers in JSCC systems for wireless image transmission remains largely unexplored. In this letter, we propose a configurable lightweight JSCC framework that incorporates a selective replacement strategy, enabling flexible substitution of standard Conv layers with DSConv layers at various layer positions and replacement ratios. By adjusting the proportion of layers replaced, we achieve different model compression levels and analyze their impact on reconstruction performance. Furthermore, we investigate how replacements at different encoder and decoder depths influence reconstruction quality under a fixed replacement ratio. Our results show that Conv-to-DSConv replacement at intermediate layers achieves a favorable complexity-performance trade-off, revealing layer-wise redundancy in DL-based JSCC systems. Extensive experiments further demonstrate that the proposed framework achieves substantial parameter reduction with only slight performance degradation, enabling flexible complexity-performance trade-offs for resource-constrained edge devices.

Related papers

• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks [73.41290017870097]
  SecDiff is a plug-and-play, diffusion-aided decoding framework.<n>It significantly enhances the security and robustness of deep J SCC under adversarial wireless environments.
  arXiv  Detail & Related papers  (2025-11-03T11:24:06Z)
• SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models [52.40011613324083]
  Joint source-channel coding systems (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission.<n>Existing methods focus on minimizing distortion between the transmitted image and the reconstructed version at the receiver, often overlooking perceptual quality.<n>We propose SING, a novel framework that formulates the recovery of high-quality images from corrupted reconstructions as an inverse problem.
  arXiv  Detail & Related papers  (2025-03-16T12:32:11Z)
• The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs [4.735670734773145]
  We show the existence of a strict tradeoff between channel rate, distortion perception, and classification accuracy. We propose two image compression methods to navigate that tradeoff: theCO algorithm and ID-GAN, which is more general compression. They also demonstrate that the proposed ID-GAN algorithm balances image distortion, perception, classification accuracy, and significantly outperforms traditional separation-based methods.
  arXiv  Detail & Related papers  (2023-12-22T16:06:43Z)
• ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification [63.49762079000726]
  We propose a convolutional broad learning system (ConvBLS) based on the spherical K-means (SKM) algorithm and two-stage multi-scale (TSMS) feature fusion. Our proposed ConvBLS method is unprecedentedly efficient and effective.
  arXiv  Detail & Related papers  (2023-04-01T04:16:12Z)
• Generative Joint Source-Channel Coding for Semantic Image Transmission [29.738666406095074]
  Joint source-channel coding (JSCC) schemes using deep neural networks (DNNs) provide promising results in wireless image transmission. We propose two novel J SCC schemes that leverage the perceptual quality of deep generative models (DGMs) for wireless image transmission.
  arXiv  Detail & Related papers  (2022-11-24T19:14:27Z)
• Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression [18.897023700334458]
  The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC) bring significant challenges to its high-efficient compression. This paper proposes a novel end-to-end, fully-factorized deep framework that encodes the original LPC into an octree structure and hierarchically decomposes the octree entropy model in layers.
  arXiv  Detail & Related papers  (2022-09-26T08:36:11Z)
• STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition [40.997546601209145]
  STD-NET is an unsupervised deep-learning architecture search approach via hierarchical tensor decomposition for image steganalysis. Our proposed strategy is more efficient and can remove more redundancy compared with previous steganalytic network compression methods.
  arXiv  Detail & Related papers  (2022-06-12T03:46:08Z)
• Image Superresolution using Scale-Recurrent Dense Network [30.75380029218373]
  Recent advances in the design of convolutional neural network (CNN) have yielded significant improvements in the performance of image super-resolution (SR) We propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs)) Our scale recurrent design delivers competitive performance for higher scale factors while being parametrically more efficient as compared to current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-01-28T09:18:43Z)
• Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding [7.081604594416339]
  We consider the scenario in which images are transmitted progressively in layers over time or frequency. DeepJSCC-$l$ is an innovative solution that uses convolutional autoencoders. DeepJSCC-$l$ has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes.
  arXiv  Detail & Related papers  (2020-09-26T00:11:50Z)
• Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior [80.5637175255349]
  We propose a new enriched prior based Dual-constrained Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net. To ex-tract hidden deep features, DS2CF-Net is modeled as a deep-structure and geometrical structure-constrained neural network. Our network can obtain state-of-the-art performance for representation learning and clustering.
  arXiv  Detail & Related papers  (2020-09-08T13:10:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.