A Preliminary Comparison Between Compressive Sampling and Anisotropic Mesh-Based Image Representation

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself. Although regular images are not sparse on their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. However, an alternative approach, adaptive sampling such as mesh-based image representation (MbIR), has not attracted as much attention. MbIR works directly on image pixels and represents the image with fewer points using a triangular mesh. In this paper, we perform a preliminary comparison between the CS and a recently developed MbIR method, AMA representation. The results demonstrate that at the same sample density, AMA representation can provide better reconstruction quality than CS based on the tested algorithms. Further investigation with recent algorithms is needed to perform a thorough comparison.

Original languageEnglish (US)
Title of host publicationIntelligent Computing - Proceedings of the 2021 Computing Conference
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030801182
StatePublished - 2022
EventComputing Conference, 2021 - Virtual, Online
Duration: Jul 15 2021Jul 16 2021

Publication series

NameLecture Notes in Networks and Systems


ConferenceComputing Conference, 2021
CityVirtual, Online


  • AMA representation
  • Compressive sampling
  • Mesh-based image representation
  • PSNR
  • Structural similarity index measure

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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