  Nowadays, deep-learning image coding solutions have shown similar or better
compression efficiency than conventional solutions based on hand-crafted
transforms and spatial prediction techniques. These deep-learning codecs
require a large training set of images and a training methodology to obtain a
suitable model (set of parameters) for efficient compression. The training is
performed with an optimization algorithm which provides a way to minimize the
loss function. Therefore, the loss function plays a key role in the overall
performance and includes a differentiable quality metric that attempts to mimic
human perception. The main objective of this paper is to study the perceptual
impact of several image quality metrics that can be used in the loss function
of the training process, through a crowdsourcing subjective image quality
assessment study. From this study, it is possible to conclude that the choice
of the quality metric is critical for the perceptual performance of the
deep-learning codec and that can vary depending on the image content.
