Coding and Entropy

Dear Colleagues,

The Special Issue focuses on new developments in multi-type coding and entropies and their applications in communications, data processing and machine learning.

Shannon’s information theory answers two fundamental questions raised by communication theory: What is the ultimate data compression, and what is the ultimate transmission rate of communication? Entropy is the core concept of this framework, with coding beings its most significant technology, including source coding, channel coding, and network coding. In this context, a number of metrics, such as Shannon entropy, Rényi entropy, message importance measure, sample entropy, fuzzy entropy, and permutation entropy, are introduced to quantify the irregularity or uncertainty of signals and images. Various coding theories and methods have also been proposed to reduce the occupancy of communication and storage resources in order to improve the objective efficiency of the communication network and the subjective experience of clients.

With advances in intelligent vision algorithms and devices, data reprocessing and secondary propagation are becoming increasingly prevalent. the production of a large amount of similar data is becoming more rapid and widespread, resulting in a homogeneity and similarity in data such as images and videos and creating new challenges for information theory. Novel entropy and coding methods may play a significant role in the era of big data.

We invite authors to submit previously unpublished contributions in any field related to developments and applications of information theory in coding and entropy, including but not limited to, the following subtopics:

• Mathematical extensions for entropy analysis;
• Source coding and channel coding techniques;
• Network coding and its related topics;
• Two- and three-dimensional entropy methods for image analysis;
• Entropy optimization and modeling for performance enhancement;
• Entropy-based image, signal processing, and coding;
• Network information theory and semantic information theory;
• Compressed sensing and rate-distortion theory;
• Application of entropy and coding in machine learning;
• Application of machine learning method to developments of coding and entropy.

Prof. Dr. Pingyi Fan
Dr. Qi Chen
Dr. Suihua Cai
Guest Editors

Gangtao Xin
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.