Sensor-layer image compression based on the quantized cosine transform

Nikos P. Pitsianis, David J. Brady, Xiaobai Sun

Research output: Contribution to journalConference articlepeer-review

25 Scopus citations


We introduce a novel approach for compressive coding at the sensor layer for an integrated imaging system. Compression at the physical layer reduces the measurements-to-pixels ratio and the data volume for storage and transmission, without confounding image estimation or analysis. We introduce a particular compressive coding scheme based on the quantized Cosine transform (QCT) and the corresponding image reconstruction scheme. The QCT is restricted on the ternary set {-1, 0, 1} for economic implementation with a focal plane optical pixel mask. Combined with the reconstruction scheme, the QCT-based coding is shown favorable over existing coding schemes from the coded aperture literature, in terms of both reconstruction quality and photon efficiency.

Original languageEnglish (US)
Article number25
Pages (from-to)250-257
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2005
Externally publishedYes
EventVisual Information Processing XIV - Orlando, FL, United States
Duration: Mar 29 2005Mar 30 2005

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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