The onboard data storage burden associated
with underwater (UW) imagery tends to be
significant for applications such as image-based navigation
via an autonomous UW vehicle (AUV). Due to
mission, space, and power requirements the use and
storage of multispectral imagery in compressed form
is preferred. In this paper, we discuss the spatial and
statistical characteristics of UW imagery that facilitate
compression by well-known algorithms such as
JPEG, vector quantization (VQ), and visual pattern
image coding (VPIC). For example, we consider statistical
distributions of target and background greylevels
obtained from truthed imagery, as well as power spectral
analysis of target-background differences. The
former measures facilitate parameter selection in VQ
and VPIC, while the latter are important in JPEG.
Preliminary results are given for recently-developed
algorithms that yield compression ratios ranging from
5,500:1 to 16,500:1 based on prefiltered six-band multispectral
imagery of resolution 720x480 pixels. The
prefiltering step, which removes unwanted background
objects, is key to achieving high compression.
Member of
Contributors
Publisher
Marine Technology Society
Date Issued
1996
Note
Language
Type
Genre
Form
Extent
9 p.
Subject (Topical)
Identifier
FA00007349
Additional Information
The onboard data storage burden associated
with underwater (UW) imagery tends to be
significant for applications such as image-based navigation
via an autonomous UW vehicle (AUV). Due to
mission, space, and power requirements the use and
storage of multispectral imagery in compressed form
is preferred. In this paper, we discuss the spatial and
statistical characteristics of UW imagery that facilitate
compression by well-known algorithms such as
JPEG, vector quantization (VQ), and visual pattern
image coding (VPIC). For example, we consider statistical
distributions of target and background greylevels
obtained from truthed imagery, as well as power spectral
analysis of target-background differences. The
former measures facilitate parameter selection in VQ
and VPIC, while the latter are important in JPEG.
Preliminary results are given for recently-developed
algorithms that yield compression ratios ranging from
5,500:1 to 16,500:1 based on prefiltered six-band multispectral
imagery of resolution 720x480 pixels. The
prefiltering step, which removes unwanted background
objects, is key to achieving high compression.
with underwater (UW) imagery tends to be
significant for applications such as image-based navigation
via an autonomous UW vehicle (AUV). Due to
mission, space, and power requirements the use and
storage of multispectral imagery in compressed form
is preferred. In this paper, we discuss the spatial and
statistical characteristics of UW imagery that facilitate
compression by well-known algorithms such as
JPEG, vector quantization (VQ), and visual pattern
image coding (VPIC). For example, we consider statistical
distributions of target and background greylevels
obtained from truthed imagery, as well as power spectral
analysis of target-background differences. The
former measures facilitate parameter selection in VQ
and VPIC, while the latter are important in JPEG.
Preliminary results are given for recently-developed
algorithms that yield compression ratios ranging from
5,500:1 to 16,500:1 based on prefiltered six-band multispectral
imagery of resolution 720x480 pixels. The
prefiltering step, which removes unwanted background
objects, is key to achieving high compression.
Florida Atlantic University. Harbor Branch Oceanographic Institute contribution 1200
This manuscript is an author version with the final
publication available and may be cited as: Schmalz, M. S., Ritter, G. X., & Caimi, F. M. (1996).
Data compression techniques for underwater imagery. In Oceans 96 MTS/IEEE: coastal ocean,
prospects for the 21st century: conference proceedings, 23-26 September, 1996, Broward
County Convention Center, Fort Lauderdale, Florida (pp. 929-936). Washington, DC: Marine
Technology Society.
publication available and may be cited as: Schmalz, M. S., Ritter, G. X., & Caimi, F. M. (1996).
Data compression techniques for underwater imagery. In Oceans 96 MTS/IEEE: coastal ocean,
prospects for the 21st century: conference proceedings, 23-26 September, 1996, Broward
County Convention Center, Fort Lauderdale, Florida (pp. 929-936). Washington, DC: Marine
Technology Society.
Date Backup
1996
Date Text
1996
DOI
10.1109/OCEANS.1996.568357
Date Issued (EDTF)
1996
Extension
FAU
IID
FA00007349
Organizations
Attributed name: Caimi, F. M.
Attributed name: Harbor Branch Oceanographic Institute
Person Preferred Name
Schmalz, Mark S.
Physical Description
9 p.
Title Plain
Data compression techniques for underwater imagery
Origin Information
1996
Marine Technology Society
Washington, DC
Place
Washington, DC
Title
Data compression techniques for underwater imagery
Other Title Info
Data compression techniques for underwater imagery