  We introduce a stop-code tolerant (SCT) approach to training recurrent
convolutional neural networks for lossy image compression. Our methods
introduce a multi-pass training method to combine the training goals of
high-quality reconstructions in areas around stop-code masking as well as in
highly-detailed areas. These methods lead to lower true bitrates for a given
recursion count, both pre- and post-entropy coding, even using unstructured
LZ77 code compression. The pre-LZ77 gains are achieved by trimming stop codes.
The post-LZ77 gains are due to the highly unequal distributions of 0/1 codes
from the SCT architectures. With these code compressions, the SCT architecture
maintains or exceeds the image quality at all compression rates compared to
JPEG and to RNN auto-encoders across the Kodak dataset. In addition, the SCT
coding results in lower variance in image quality across the extent of the
image, a characteristic that has been shown to be important in human ratings of
image quality
