  Learning-based image compression methods have emerged as state-of-the-art,
showcasing higher performance compared to conventional compression solutions.
These data-driven approaches aim to learn the parameters of a neural network
model through iterative training on large amounts of data. The optimization
process typically involves minimizing the distortion between the decoded and
the original ground truth images. This paper focuses on perceptual optimization
of learning-based image compression solutions and proposes: i) novel loss
function to be used during training and ii) novel subjective test methodology
that aims to evaluate the decoded image fidelity. According to experimental
results from the subjective test taken with the new methodology, the
optimization procedure can enhance image quality for low-rates while offering
no advantage for high-rates.
