EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation

Körber, Nikolai; Kromer, Eduard; Siebert, Andreas; Hauke, Sascha; Mueller-Gritschneder, Daniel; Schuller, Björn

doi:10.1007/978-3-031-72761-0_12

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2024

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Cited by 3 publications

References 30 publications

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Rate-Distortion-Cognition Controllable Versatile Neural Image Compression

Liu,

Feng,

et al. 2024

Lecture Notes in Computer Science

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Rate-Distortion-Cognition Controllable Versatile Neural Image Compression

Liu,

Feng,

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Dual-Conditioned Training to Exploit Pre-Trained Codebook-Based Generative Model in Image Compression

Iwai,

Miyazaki,

Omachi

2024

IEEE Access

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Deep Learning Application for Biodiversity Conservation and Educational Tourism in Natural Reserves

Flórez,

Becerra,

Carrillo

et al. 2024

IJGI

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Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists.

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EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation

Cited by 3 publications

References 30 publications

Rate-Distortion-Cognition Controllable Versatile Neural Image Compression

Rate-Distortion-Cognition Controllable Versatile Neural Image Compression

Dual-Conditioned Training to Exploit Pre-Trained Codebook-Based Generative Model in Image Compression

Deep Learning Application for Biodiversity Conservation and Educational Tourism in Natural Reserves

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