Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios
<|reference_start|>Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios: 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<|reference_end|>
@article{pérez-gonzalo2024generalized,
    title={Generalized Nested Latent Variable Models for Lossy Coding applied to
  Wind Turbine Scenarios},
    author={Ra"ul P'erez-Gonzalo, Andreas Espersen, Antonio Agudo},
    journal={arXiv preprint arXiv:2406.06165},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06165},
    primaryClass={cs.CV cs.AI cs.IT cs.LG math.IT}
}