  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
