Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914830615576576 |
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| author | Pérez-Gonzalo, Raül Espersen, Andreas Agudo, Antonio |
| author_facet | Pérez-Gonzalo, Raül Espersen, Andreas Agudo, Antonio |
| contents | Rate-distortion optimization through neural networks has accomplished competitive results in compression efficiency and image quality. This learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality by automatically extracting and retaining crucial information, while discarding less critical details. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model, enhancing compression by capturing complex data dependencies. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure. We demonstrate as L increases that a trainable prior is detrimental and explore a common dimensionality along the distinct latent variables to boost compression performance. As this structured framework can represent autoregressive coders, we outperform the hyperprior model and achieve state-of-the-art performance while reducing substantially the computational cost. Our experimental evaluation is performed on wind turbine scenarios to study its application on visual inspections |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06165 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios Pérez-Gonzalo, Raül Espersen, Andreas Agudo, Antonio Computer Vision and Pattern Recognition Artificial Intelligence Information Theory Machine Learning Rate-distortion optimization through neural networks has accomplished competitive results in compression efficiency and image quality. This learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality by automatically extracting and retaining crucial information, while discarding less critical details. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model, enhancing compression by capturing complex data dependencies. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure. We demonstrate as L increases that a trainable prior is detrimental and explore a common dimensionality along the distinct latent variables to boost compression performance. As this structured framework can represent autoregressive coders, we outperform the hyperprior model and achieve state-of-the-art performance while reducing substantially the computational cost. Our experimental evaluation is performed on wind turbine scenarios to study its application on visual inspections |
| title | Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Information Theory Machine Learning |
| url | https://arxiv.org/abs/2406.06165 |