  The advent of the internet, followed shortly by the social media made it
ubiquitous in consuming and sharing information between anyone with access to
it. The evolution in the consumption of media driven by this change, led to the
emergence of images as means to express oneself, convey information and
convince others efficiently. With computer vision algorithms progressing
radically over the last decade, it is become easier and easier to study at
scale the role of images in the flow of information online. While the research
questions and overall pipelines differ radically, almost all start with a
crucial first step - evaluation of global perceptual similarity between
different images. That initial step is crucial for overall pipeline performance
and processes most images. A number of algorithms are available and currently
used to perform it, but so far no comprehensive review was available to guide
the choice of researchers as to the choice of an algorithm best suited to their
question, assumptions and computational resources. With this paper we aim to
fill this gap, showing that classical computer vision methods are not
necessarily the best approach, whereas a pair of relatively little used methods
- Dhash perceptual hash and SimCLR v2 ResNets achieve excellent performance,
scale well and are computationally efficient.
