On the class of coding optimality of human languages and the origins of Zipf's law

Here we present a new class of optimality for coding systems. Members of that class are separated linearly from optimal coding and thus exhibit Zipf's law, namely a power-law distribution of frequency ranks. Whithin that class, Zipf's law, the size-rank law and the size-probability law form a group-like structure. We identify human languages that are members of the class. All languages showing sufficient agreement with Zipf's law are potential members of the class. In contrast, there are communication systems in other species that cannot be members of that class for exhibiting an exponential distribution instead but dolphins and humpback whales might. We provide a new insight into plots of frequency versus rank in double logarithmic scale. For any system, a straight line in that scale indicates that the lengths of optimal codes under non-singular coding and under uniquely decodable encoding are separated by a linear function whose slope is the exponent of Zipf's law. For systems under compression and constrained to be uniquely decodable, such a straight line may indicate that the system is coding close to optimality. Our findings provide support for the hypothesis that Zipf's law originates from compression.