  Neural-based image and video codecs are significantly more power-efficient
when weights and activations are quantized to low-precision integers. While
there are general-purpose techniques for reducing quantization effects, large
losses can occur when specific entropy coding properties are not considered.
This work analyzes how entropy coding is affected by parameter quantizations,
and provides a method to minimize losses. It is shown that, by using a certain
type of coding parameters to be learned, uniform quantization becomes
practically optimal, also simplifying the minimization of code memory
requirements. The mathematical properties of the new representation are
presented, and its effectiveness is demonstrated by coding experiments, showing
that good results can be obtained with precision as low as 4~bits per network
output, and practically no loss with 8~bits.
