Visual Information Evaluation with Entropy of Primitive

Songchao TAN*, Shurun WANG, Xiang ZHANG, Shanshe WANG, Shiqi WANG, Siwei MA, Wen GAO

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Citations (Scopus)
27 Downloads (CityUHK Scholars)

Abstract

In this paper, we overview the recent work on entropy of primitive (EoP), including its concept, design, extension and mathematical analysis in evaluating the visual information of natural images. The design philosophy of EoP is establishing an entropy model that quantifies the visual information based on patch-level sparse representation, due to the close relationship between sparse representation and the hierarchical cognitive process of human perception. Furthermore, based on the concept and definition of EoP, we also demonstrate several applications, including just noticeable difference estimation, visual quality assessment, etc. The future research directions of visual information evaluation are also envisioned, where we can perceive both promises and challenges.
Original languageEnglish
Pages (from-to)31750-31758
JournalIEEE Access
Volume6
Online published18 Apr 2018
DOIs
Publication statusPublished - 2018

Research Keywords

  • Dictionaries
  • Entropy
  • Entropy of primitive
  • Estimation
  • Image reconstruction
  • just noticeable difference
  • Matching pursuit algorithms
  • Quality assessment
  • quality assessment
  • sparse representation
  • visual information
  • Visualization

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