  This paper considers lossless image compression and presents a learned
compression system that can achieve state-of-the-art lossless compression
performance but uses only 59K parameters, which is more than 30x less than
other learned systems proposed recently in the literature. The explored system
is based on a learned pixel-by-pixel lossless image compression method, where
each pixel's probability distribution parameters are obtained by processing the
pixel's causal neighborhood (i.e. previously encoded/decoded pixels) with a
simple neural network comprising 59K parameters. This causality causes the
decoder to operate sequentially, i.e. the neural network has to be evaluated
for each pixel sequentially, which increases decoding time significantly with
common GPU software and hardware. To reduce the decoding time, parallel
decoding algorithms are proposed and implemented. The obtained lossless image
compression system is compared to traditional and learned systems in the
literature in terms of compression performance, encoding-decoding times and
computational complexity.
