  In this manuscript we propose two objective terms for neural image
compression: a compression objective and a cycle loss. These terms are applied
on the encoder output of an autoencoder and are used in combination with
reconstruction losses. The compression objective encourages sparsity and low
entropy in the activations. The cycle loss term represents the distortion
between encoder outputs computed from the original image and from the
reconstructed image (code-domain distortion). We train different autoencoders
by using the compression objective in combination with different losses: a)
MSE, b) MSE and MSSSIM, c) MSE, MS-SSIM and cycle loss. We observe that images
encoded by these differently-trained autoencoders fall into different points of
the perception-distortion curve (while having similar bit-rates). In
particular, MSE-only training favors low image-domain distortion, whereas cycle
loss training favors high perceptual quality.
