  The long-standing theory that a colour-naming system evolves under dual
pressure of efficient communication and perceptual mechanism is supported by
more and more linguistic studies, including analysing four decades of
diachronic data from the Nafaanra language. This inspires us to explore whether
machine learning could evolve and discover a similar colour-naming system via
optimising the communication efficiency represented by high-level recognition
performance. Here, we propose a novel colour quantisation transformer,
CQFormer, that quantises colour space while maintaining the accuracy of machine
recognition on the quantised images. Given an RGB image, Annotation Branch maps
it into an index map before generating the quantised image with a colour
palette; meanwhile the Palette Branch utilises a key-point detection way to
find proper colours in the palette among the whole colour space. By interacting
with colour annotation, CQFormer is able to balance both the machine vision
accuracy and colour perceptual structure such as distinct and stable colour
distribution for discovered colour system. Very interestingly, we even observe
the consistent evolution pattern between our artificial colour system and basic
colour terms across human languages. Besides, our colour quantisation method
also offers an efficient quantisation method that effectively compresses the
image storage while maintaining high performance in high-level recognition
tasks such as classification and detection. Extensive experiments demonstrate
the superior performance of our method with extremely low bit-rate colours,
showing potential to integrate into quantisation network to quantities from
image to network activation. The source code is available at
https://github.com/ryeocthiv/CQFormer
