Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios

• URL: http://arxiv.org/abs/2406.06165v1
• Date: Mon, 10 Jun 2024 11:00:26 GMT
• Title: Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios
• Authors: Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo,
• Abstract summary: A learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure.
• Score: 14.48369551534582
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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