Title: Dynamic image compression and reconstruction via tensor decomposition on edge nodes

Authors: Zhaohua Zeng; Xiaoxia Wang

Addresses: Department of Big Data and Intelligent Engineering, Shanxi Institute of Engineering Technology, Yangquan, 045000, China ' Department of Big Data and Intelligent Engineering, Shanxi Institute of Engineering Technology, Yangquan, 045000, China

Abstract: In wireless multimedia sensor networks, edge nodes are constrained by computational resources and energy supply, necessitating an efficient balance between image compression and reconstruction. To address this, this paper employs block-sparse tensor-based compression coding for images. A virtual codebook pool with block-sparse characteristics is trained based on image texture features, utilising Tucker decomposition and fractal coding for greyscale matching. Building upon this, a hierarchical clustered network topology is designed to collaboratively perform image decomposition, compression, and reconstruction at edge nodes. To enhance image reconstruction quality, a dynamic image reconstruction model based on block-sparse tensor decomposition and the transformer architecture is proposed. Block-sparse tensor decomposition is embedded within the transformer to learn global information of the image. Experimental results demonstrate that the proposed method achieves a network energy consumption of only 397.48 nJ/bit, with a peak signal-to-noise ratio of 38.86 dB.

Keywords: wireless multimedia sensor network; WMSN; edge nodes; tensor decomposition; image compression and reconstruction; transformer model.

DOI: 10.1504/IJSNET.2026.152184

International Journal of Sensor Networks, 2026 Vol.50 No.3, pp.161 - 173

Received: 29 Sep 2025
Accepted: 03 Oct 2025
Published online: 10 Mar 2026 *

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