How to represent an image as state in a Q-table

Question

I'm trying to do Q-learning with the Atari games using the gym python's package.

I want to use the image as the state of my algorithm, but I came up with a doubt: Is the state all the possibilities for all the pixels of the image?

I've seen many Q-learning implementations, and in the Q-table they always store all the possible states as rows, and the actions as columns (for example, https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/)

Without doing maths, all the possible states for, let's say, a 200x200 grayscale image are insane. How can I handle this problem without doing neural networks?

I guess there must be an approach for this scenario, but I haven't found any good example.

Thank you!!