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Signal AcquisitionWilliam Blackwell, MIT Lincoln Laboratory
Signal ProcessingHenrique Malvar, Microsoft Research
Signal Processing Application & VenturesJeffrey Bernstein, Analog DevicesBrian Hinman, Oak Investment Partners
StaelinFest-1WJB 7/18/2011
Dave Staelin’s Influence on Microwave & Hyperspectral
Infrared Remote SensingBill Blackwell (Staelin Ph.D., 2002)
StaelinFest
18 July 2011
StaelinFest-2WJB 7/18/2011
Dave's Gift (one of many):Elegant Thinking about Complex Problems
First slide of course notes fromMIT EECS 6.661: Receivers, Antennas, and Signals
StaelinFest-3WJB 7/18/2011
• A New Generation of Satellite Sensors– Hyperspectral IR spectrometers: From RGB to 1000’s of “colors”– Opportunities for new signal processing methods
• A New Generation of Remote Sensing Systems– NASA Aqua (Dave’s methods now used operationally)– NPOESS/JPSS (Dave’s methods to be used operationally)– DWSS (Dave’s methods to be used operationally)
• A Look Forward and A Look Back
Principal Components
StaelinFest-5WJB 7/18/2011
Passive Microwave Sensing of Precipitation
35 km
45 k
m
Image credit: Vince Leslie (Staelin Ph.D., 2004)
StaelinFest-6WJB 7/18/2011
Atmospheric Transmission at Microwave Wavelengths
The frequency dependence of atmospheric absorption allows different altitudes to be sensed by spacing channels along absorption lines
“Sounding” channels
StaelinFest-7WJB 7/18/2011
Synergistic Use of MW+IR:Infrared Provides High Spatial Resolution
Typical infrared sensors provide 15km horizontal and 1km vertical resolution
StaelinFest-8WJB 7/18/2011
Passive Microwave Measurements Provide Low Spatial Resolution, but Penetrate Clouds
Typical microwave sensors provide 35km horizontal and 3km vertical resolution
Dave’s contributions span both the spectral and spatial domains
StaelinFest-9WJB 7/18/2011
Innovation in Algorithm Development: Neural Network Retrievals
AIRS measures upwelling thermal emission in 2378 spectral bands from 4-15 µmSounding is accomplished using absorption near CO2 lines
AMSU measures upwelling thermal emission in 20 spectral bands near 60 and 183 GHzSounding is accomplished using absorption near O2 (60-GHz) and H2O (183-GHz) lines
50 k
mSCC/NN Algorithm:
• 100X faster than state-of-art• Improved accuracy and precision• Enables rapid processing of
multi-year climate records
StaelinFest-10WJB 7/18/2011
US Next-Generation Weather Satellite ProgramOctober 25, 2011 Launch
NPP spacecraft photo courtesy of Ball Aerospace, Boulder, CO.
“Dave’s Instrument”
StaelinFest-12WJB 7/18/2011
• FUN times: He loves his work and it is contagious– Many great summer parties
• ALWAYS a champion for his students• A man of many accomplishments
– #1 (by far) was marrying Ellen
• "Life Lessons" (of which there were many)– Management– Psychology– Politics– Art of engineering
• A lasting legacy– Educator– Innovator– Mentor– Friend
Reflections on Working with Dave
Blackwell wedding, Aug 2000
Jack and Ann Barrett
Signal AcquisitionWilliam Blackwell, MIT Lincoln Laboratory
Signal ProcessingHenrique Malvar, Microsoft Research
Signal Processing Application & VenturesJeffrey Bernstein, Analog DevicesBrian Hinman, Oak Investment Partners
Contents• Signal Processing is very broad
– So we’ll zoom into “Lapped Transforms”– Dave pioneered the research and coined the name
Lapped Orthogonal Transform
• Contents– LT Basics– Applications– Example: Image Compression with JPEG XR
(Motivated by Dave’s question: “Rico, tell me more about JPEG XR”)
StaelinFest – MIT, July 18, 2011 Signal Processing 2
3
Block processing
• Signal is reconstructed as a linearcombination of basis functions
• Can lead to discontinuities across blockboundaries (blocking artifacts, as in JPEG)
ExtractBlock
DirectOrthogonalTransform
InverseOrthogonalTransform
AppendBlock
I nputSignal
OutputSignal
X xT= Px
~ ~x X= P
~XCoding, filtering, etc.
StaelinFest – MIT, July 18, 2011 Signal Processing
Lapped transforms• Basis functions have tails beyond block boundaries
– Linear combinations of overlapping functions such as
– generate smooth signals, without blocking artifacts:
StaelinFest – MIT, July 18, 2011 Signal Processing 4
5
Basis functions
StaelinFest – MIT, July 18, 2011Signal Processing
Discrete Cosine Transform( DCT )
basis functions end abruptly at block boundaries
Lapped Orthogonal Transform( LOT )
basis functions decay smoothly to zero beyond block boundaries
LOTs: properties• Functions can be orthogonal
and lapped orthogonal(Staelin, Cassereau, ’85)– W is one-block overlap operator
• Fast-computable via DCT (Staelin, Malvar, ’86)
StaelinFest – MIT, July 18, 2011 Signal Processing 6
T
T
==
=
P P IP W P I
0 IW
0 0
Z
E
ODCT
E
ODCT
-- -
input block2-N samples
N/2 even-symmetricLOT coefficients
N/2 odd-symmetricLOT coefficients
LOTs: design evolution• Linear-phase, DCT-based (LOT ’85)
– Best for images• DCT-IV based, good filter banks (MLT ’88)
– Best for audio• Extended multi-block overlap (ELT ’92)
– Best for modems / digital communication• Biorthogonal, hierarchical (LBT, HLBT ’99)
– Best for images, with multi-resolution• Integer-reversible (PTC ’02, JPEG XR ’08)
– Best for image compression
StaelinFest – MIT, July 18, 2011 Signal Processing 7
Applications of LTs• Audio and speech format standards (MLT)
– Audio: MP3, AAC, WMA, PAC, …– Wideband speech: G.722.1, CELT
• DSL modems (ELT)– Multitone modulation w/ large # of subbands
• Image compression (LBT)– PTC image coder (used in some Xbox games)– JPEG XR – ITU-T/ISO standard, successor to JPEG
StaelinFest – MIT, July 18, 2011 Signal Processing 8
Original LBT construction
x(0) X(0)
x(1) X(2)
X(1)
X(3)
to previous block
to next block
X(0)
X(2)
X(1)
X(3)
x(2)
x(3)
x(0) X(0)
x(1) X(2)
DCT
X(1)
X(3)
x(2)
x(3)
Zangle = /8
a = (direct)2
1/2a = (inverse)
DCT
c
c
StaelinFest – MIT, July 18, 2011 Signal Processing 10
New LBT with pre/post-filters
StaelinFest – MIT, July 18, 2011 Signal Processing 11
x(0) X(0)x(1) X(2)
DCT-like
X(1)X(3)
to previous block
to next block
x(2)x(3)
New Length-4LBT, Direct
pre-overlap
y(0)Y(0)y(1)Y(2)
Y(1)Y(3)
y(2)y(3)
IDCT-like
post-overlapfrom previous block
from previous block
New Length-4LBT, Inverse
codi
ng
overlappre-filter
overlappre-filter
overlappost-filter
overlappost-filter
Reversible & non-separable 2D LBTs
Stage 1
Stage 2
Forward transform shown, inverse is similar
Step 1.1 – HT applied to corners
Step 1.2 – HT applied to centers
Step 1.3 – HT applied to edges
Step 1.4 – HT applied to edges
Step 2.1 – HT for even-even basis
Step 2.2 – oddT for even-odd basis
Step 2.3 – oddT for odd-even basis
Step 2.4 – odd-oddT for odd-odd basis
A C B D
a b c d
1−
1−
1−
1−
1−2
1− 21
A B C D
1−
1−
21
21− 2
1
21−16
3
163−
83−
83
1−
163
163−
a b c d
a b c d
A B C D
1−2
1
21− 2
1 1−
21−8
3
43−
1−
1−8
3
StaelinFest – MIT, July 18, 2011 Signal Processing 12
Basic JPEG XR architecture
13
Reversible color
conversionTiling
Reversible HLBTTransform
Quantization, Entropy code
0110010101…
Packetization
YCoCgcolor space
StaelinFest – MIT, July 18, 2011 Signal Processing
Key aspects of JPEG XR• Supports 8, 16, 24, 32 bpc & 32-bit floats• Decoder spec 100% integer arithmetic• Lossless & lossy coding in same codec• Compression ≈ JPEG 2000, ≈ 2x JPEG• Complexity ≈ 1.3x JPEG ≈ JPEG 2000 / 3• Single-company IP, licensed royalty-free• ITU-T & ISO Standard• Commercial apps: few cameras, WP 7, IE 9, …
StaelinFest – MIT, July 18, 2011 Signal Processing 14
Signal AcquisitionWilliam Blackwell, MIT Lincoln Laboratory
Signal ProcessingHenrique Malvar, Microsoft Research
Signal Processing Application & VenturesJeffrey Bernstein, Analog DevicesBrian Hinman, Oak Investment Partners
The Hybrid MC/DCT Coding Algorithm
Jain and Jain: Displacement Measurement and Interframe Image Coding,IEEE Transactions on Communications, COM-29, No. 12, December 1981
The Short-Space Fourier Transform (SSFT)
Bernstein Thesis, “Properties and Applications of the Short-Space FourierTransform,” May 1984
SSFT versus DCT in Hybrid Video Coder
Bernstein Thesis, “Properties and Applications of the Short-Space FourierTransform,” May 1984
Lapped Orthogonal Transforms
• Finite extent basis functions:Encoding of images based on a lapped orthogonal transform, Cassereau, de Jager and Staelin, IEEE Transactions on Communications, February 1989
• Optimally derived LOT:The LOT: Transform Coding Without Blocking Effects, Malvar and Staelin, IEEE Transactions on ASSP, April 1989
Visualizing Motion Estimation
Hinman Thesis, “Theory and Applications of Image Motion Estimation,” May 1984
Early Concepts in Block Motion Estimation
STEP 1:
Picking a good initial motion vector based upon motion vectors from surrounding blocks
Hinman Thesis, “Theory and Applications of Image Motion Estimation,” May 1984
STEP 2:
Finding the motion vector through steepest decent motion estimation
Resolution Preserving Frame Interpolation
Hinman Thesis, “Theory and Applications of Image Motion Estimation,” May 1984
Rejection from Venture Capitalists
“You should stop playing part-time businessman and your two students should find jobs at Raytheon to get some experience.”
Robert G. BarrettBattery VenturesJuly 1984
Dave Gathers the PicTel MIT Team
• Dave Staelin – Chairman• Norm Gaut – Director and later CEO• Brian Hinman – V.P. Research, then V.P. Engineering• Jeff Bernstein – Principal Engineer• Rico Malvar – Video Designer and later V.P. Research• Mike Dertouzos – Consultant• Greg Papadopoulos – Processor Architect• Richard Soley – Microcode Development Environment
Our First Product: C-2000 Video Codec
• Video Resolution: 256 x 240• Frame Rate: 15fps• Transmission Rate: 56kbps – 224kbps• Processors: 7 microcoded array processors• Total Compute Power: 200 MOPS• Power Consumption: 1,350 Watts• Weight: 200 lbs
The Use of ASICs: C-3000 Video Codec
• Video Resolution: 256 x 240• Frame Rate: 15fps• Transmission Rate: 56kbps – 384kbps• Processor: LSI Logic Gate Arrays• Total Compute: 240 MOPS• Power Consumption: 420 Watts• Weight: 70 lbs
First Integrated Roll-About: V-3100
• Integrated PTZ Camera• Picture-in-Picture Video Display• Still Graphics Sub-channel• Integrated Audio with Acoustic Echo Cancellation• Control Panel with Far-end Camera Control
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