Redundancy and Optimization of tANS Entropy Encoders

Ian Blanes, Miguel Hernandez-Cabronero, Joan Serra-Sagrista, Michael W. Marcellin

Research output: Contribution to journalArticlepeer-review

Abstract

Nowadays entropy encoders are part of almost all data compression methods, with the Asymmetrical Numeral Systems (ANS) family of entropy encoders having recently risen in popularity. Entropy encoders based on the tabled variant of ANS are known to provide varying performances depending on their internal design. In this paper, we present a method that calculates encoder redundancies in almost linear time, which translates in practice to thousand-fold speedups in redundancy calculations for small automatons, and allows redundancy calculations for automatons with tens of millions of states that would be otherwise prohibitive. We also address the problem of improving tabled ANS encoder designs, by employing the aforementioned redundancy calculation method in conjunction with a stochastic hill climbing strategy. The proposed approach consistently outperforms state-of-the-art methods in tabled ANS encoder design. For automatons of twice the alphabet size, experimental results show redundancy reductions around 10% over the default initialization method and over 30% for random initialization.

Original languageEnglish (US)
JournalIEEE Transactions on Multimedia
DOIs
StateAccepted/In press - 2020

Keywords

  • Entropy encoder redundancy
  • Optimization
  • Tabled Asymmetrical Numeral Systems

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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