  In image processing, the amount of data to be processed grows rapidly, in
particular when imaging methods yield images of more than two dimensions or
time series of images. Thus, efficient processing is a challenge, as data sizes
may push even supercomputers to their limits. Quantum image processing promises
to encode images with logarithmically less qubits than classical pixels in the
image. In theory, this is a huge progress, but so far not many experiments have
been conducted in practice, in particular on real backends. Often, the precise
conversion of classical data to quantum states, the exact implementation, and
the interpretation of the measurements in the classical context are
challenging. We investigate these practical questions in this paper. In
particular, we study the feasibility of the Flexible Representation of Quantum
Images (FRQI). Furthermore, we check experimentally what is the limit in the
current noisy intermediate-scale quantum era, i.e. up to which image size an
image can be encoded, both on simulators and on real backends. Finally, we
propose a method for simplifying the circuits needed for the FRQI. With our
alteration, the number of gates needed, especially of the error-prone
controlled-NOT gates, can be reduced. As a consequence, the size of manageable
images increases.
