Sensor-layer image compression based on the quantized cosine transform

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

doi:10.1117/12.605659

Sensor-layer image compression based on the quantized cosine transform

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

Research output: Contribution to journal › Conference article › peer-review

25 Scopus citations

Abstract

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 language	English (US)
Article number	25
Pages (from-to)	250-257
Number of pages	8
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5817
DOIs	https://doi.org/10.1117/12.605659
State	Published - 2005
Externally published	Yes
Event	Visual Information Processing XIV - Orlando, FL, United States Duration: Mar 29 2005 → Mar 30 2005

ASJC Scopus subject areas

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

Access to Document

10.1117/12.605659

Cite this

@article{7c91b9b1232b4236abcf9599887d06a7,

title = "Sensor-layer image compression based on the quantized cosine transform",

abstract = "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.",

author = "Pitsianis, {Nikos P.} and Brady, {David J.} and Xiaobai Sun",

year = "2005",

doi = "10.1117/12.605659",

language = "English (US)",

volume = "5817",

pages = "250--257",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Visual Information Processing XIV ; Conference date: 29-03-2005 Through 30-03-2005",

}

TY - JOUR

T1 - Sensor-layer image compression based on the quantized cosine transform

AU - Pitsianis, Nikos P.

AU - Brady, David J.

AU - Sun, Xiaobai

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=27644556933&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27644556933&partnerID=8YFLogxK

U2 - 10.1117/12.605659

DO - 10.1117/12.605659

M3 - Conference article

AN - SCOPUS:27644556933

SN - 0277-786X

VL - 5817

SP - 250

EP - 257

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

M1 - 25

T2 - Visual Information Processing XIV

Y2 - 29 March 2005 through 30 March 2005

ER -

Sensor-layer image compression based on the quantized cosine transform

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this