A
group of researchers at Google appear to have taken a leaf out of HBO comedy
Silicon Valley’s book for its latest project. The team has developed a way to
use neural networks that mimic the workings of the human brain to compress
images more efficiently than traditional methods.
The
researchers trained an AI system (built using Google’s TensorFlow, which the
company open sourced last year) to learn how compression works using 6 million
photos for reference. It broke these images into 32 x 32 pixel pieces and
selected 100 pieces with the least effective compression to learn from; the
idea is that training with these difficult bits would make it a cakewalk to
handle the rest of the image.
The
AI then predicts how a image would look after it’s compressed and generates
that result. In addition to beating standard JPEG compression techniques in
reducing file sizes, the neural networks can also decide the best method for
compress separate parts of a given picture.
However,
the system isn’t perfect; in compressing image files, it can sometimes generate
results that don’t look good to the human eye. There isn’t yet a standardized
way to test for this. As such, the AI isn’t yet ready for prime-time. But the
research team’s progress is certainly impressive, and it’ll be interesting to
follow this project in the future.
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