  Lossless image compression is an important task in the field of multimedia
communication. Traditional image codecs typically support lossless mode, such
as WebP, JPEG2000, FLIF. Recently, deep learning based approaches have started
to show the potential at this point. HyperPrior is an effective technique
proposed for lossy image compression. This paper generalizes the hyperprior
from lossy model to lossless compression, and proposes a L2-norm term into the
loss function to speed up training procedure. Besides, this paper also
investigated different parameterized models for latent codes, and propose to
use Gaussian mixture likelihoods to achieve adaptive and flexible context
models. Experimental results validate our method can outperform existing deep
learning based lossless compression, and outperform the JPEG2000 and WebP for
JPG images.
