DL model to assess quality of image I have an idea but I am not certain that it can be modeled in a DL architecture.

Let's say we have images of different qualities based on color patterns and their assessment as labels in a range from 0-1.
E.g. Image 1 has 0.25 quality, Image 2 has 0.5 quality and so on.

Could this be implemented in a standard Resnet50 architecture with 1 output and sigmoid?
Is there anything in literature that you could point me up to? I wasn't able to find anything, maybe I am searching it wrong.

EDIT: I have found this https://github.com/idealo/image-quality-assessment, but the implementation is different that I suggest. I know that this can work if I add five different quality classes. 

I want to know if I can train this with 1 class output with a rank, in order to let the model understand how those qualities are linked. For instance quality 2 is the next best quality than quality 1.
I found the answer I was looking for in this Google paper:

However, in the case of ordered-classes (e.g.
aesthetic and quality estimation), cross-entropy loss lacks the
inter-class relationships between score buckets. One might
argue that ordered-classes can be represented by a real number, and consequently, can be learned through a regression
framework. Yet, it has been shown that for ordered classes,
the classification frameworks can outperform regression models [21], [31]. Hou et al. [21] show that training on datasets
with intrinsic ordering between classes can benefit from EMDbased losses. These loss functions penalize mis-classifications according to class distances