A Switch to the Concern of User: Importance Coefficient in Utility Distribution and Message Importance Measure

This paper mainly focuses on the utilization frequency in receiving end of communication systems, which shows the inclination of the user about different symbols. When the average number of use is limited, a specific utility distribution is proposed on the best effort in term of fairness, which is also the closest one to occurring probability in the relative entropy. Similar to a switch, its parameter can be selected to make it satisfy different users' requirements: negative parameter means the user focus on high-probability events and positive parameter means the user is interested in small-probability events. In fact, the utility distribution is a measure of message importance in essence. It illustrates the meaning of message importance measure (MIM), and extend it to the general case by selecting the parameter. Numerical results show that this utility distribution characterizes the message importance like MIM and its parameter determines the concern of users.Comment: 5 pages, 3 figure

Storage Space Allocation Strategy for Digital Data with Message Importance

This paper mainly focuses on the problem of lossy compression storage from the perspective of message importance when the reconstructed data pursues the least distortion within limited total storage size. For this purpose, we transform this problem to an optimization by means of the importance-weighted reconstruction error in data reconstruction. Based on it, this paper puts forward an optimal allocation strategy in the storage of digital data by a kind of restrictive water-filling. That is, it is a high efficient adaptive compression strategy since it can make rational use of all the storage space. It also characterizes the trade-off between the relative weighted reconstruction error and the available storage size. Furthermore, this paper also presents that both the users' preferences and the special characteristic of data distribution can trigger the small-probability event scenarios where only a fraction of data can cover the vast majority of users' interests. Whether it is for one of the reasons above, the data with highly clustered message importance is beneficial to compression storage. In contrast, the data with uniform information distribution is incompressible, which is consistent with that in information theory.Comment: 34pages, 7 figure