The authors discuss the underlying principles of image and video compression. The network model they use for image compression is the random neural network (RNN). This pulsed network model provides a somewhat more accurate representation of what occurs in "real" neurons. Signals in the form of pulse trains travel between neurons. These pulses can be either excitatory (we call these "positive" pulses), or they can be inhibitory or "negative". Just like many naturally occurring neural nets, these pulses all have the same magnitude which is normalized as 1. A neuron in the RNN emits pulses at an instantaneous rate proportional to its degree of excitation and to its rate of firing. Besides being more accurate, the RNN is also useful because an algorithm, which allows for the training of a fully recurrent RNN, has been designed. This means it is possible to find good weights between neurons even if every neuron has a connection to every other neuron. This full recurrence is not easily allowed in standard back propagation networks.