End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines
📰 ArXiv cs.AI
End-to-end image compression with segmentation guided dual coding for wind turbine inspections
Action Steps
- Propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode compression
- Implement a segmentation module to accurately identify blade regions in images
- Use the segmentation output to guide dual-mode compression, preserving high fidelity in blade regions and aggressively compressing the background
- Evaluate the framework's performance on wind turbine inspection images
Who Needs to Know This
Computer vision engineers and data scientists on a team can benefit from this research as it provides an efficient solution for image compression in wind turbine inspections, enabling faster defect detection and assessment.
Key Insight
💡 Segmentation guided dual coding can efficiently preserve high fidelity in critical regions while compressing background areas
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💡 End-to-end image compression for wind turbine inspections with segmentation guided dual coding
Key Takeaways
End-to-end image compression with segmentation guided dual coding for wind turbine inspections
Full Article
Title: End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines
Abstract:
arXiv:2603.29927v1 Announce Type: cross 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 identifi
Abstract:
arXiv:2603.29927v1 Announce Type: cross 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 identifi
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