[2603.29927] End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.29927 (cs) [Submitted on 31 Mar 2026] Title:End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines Authors:Raül Pérez-Gonzalo, Andreas Espersen, Søren Forchhammer, Antonio Agudo View a PDF of the paper titled End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines, by Ra\"ul P\'erez-Gonzalo and 3 other authors View PDF HTML (experimental) Abstract:Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy and lossless) compression. The segmentation module accurately identifies the blade region, after which our region-of-interest (ROI) compressor encodes it at superior quality compared to the rest of the image. Unlike conventional ROI schemes that merely allocate more bits to salient areas, our framework integrates: (i) a robust segmentation network (BU-Netv2+P) with a CRF-regularized loss for precise blade localization, (ii) a hyperprior-based autoencoder optimized for lossy compression, and (iii) an extended bits-back coder with hierarchical models for fully lossless blade reconstruction. Furthermore, our ROI framework removes the s...