Abstract: Known systems use two probability signals for coding an input signal into an output signal and for decoding said output signal into the input signal. In a first memory field two probability signals are stored, and in a second memory field two other probability signals are stored, and in response to a further received symbol processor means adapt the probability signals. Such a system needs a giant memory capacity, which can be reduced by combining probability signals into a first combination signal to be stored in the first memory field and by combining further probability signals into a second combination signal to be stored in the second memory field, and by converting a third combination signal into at least one probability signal just before (de)coding takes place.

Abstract: Known methods for construct a tree, for a context tree algorithm for coding symbols, construct trees by adding nodes. These methods need a giant memory capacity, which can be reduced in case unnecessary nodes are not added to the tree. This should be done through dependence on values of parameters belonging to a node where each parameter defines a number of symbols received that have a certain value.

Abstract: Known systems use two probability signals for coding an input signal into an output signal and for decoding said output signal into the input signal. In a first memory field two probability signals are stored, and in a second memory field two other probability signals are stored, and in response to a further received symbol processor means adapt the probability signals. Such a system needs a giant memory capacity, which can be reduced by combining probability signals into a first combination signal to be stored in the first memory field and by combining further probability signals into a second combination signal to be stored in the second memory field, and by converting a third combination signal into at least one probability signal just before (de)coding takes place.