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Institute of Electronics, National Chiao Tung University
Scalable Extension of H.264/AVC
Student: Hung-Chih Lin
Advisor: Prof. Hsueh-Ming Hang
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References• [1] Reichel, J., Hanke, K., Popescu, B.: Scalable
Video Coding V1.0. ISO/IEC JTC1/SC29/WG11, N6372 (2004)
• [2] H. Schwarz, D. Marpe, and T. Wiegand, “Scalable Extension of H.264/AVC”, ISO/IEC JTC1/WG11 Doc. M10569/S03, Mar. 2004.
• [3] I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps”, J. Fourier Anal. Appl. 4(3), pp. 245-267, 1998.
• [4] J. Reichel, H. Schwarz, and M.Wien, "Joint Scalable Video Model JSVM-2," 17th JVT meeting, JVT-Q202, Nice, France.
• [5] Tabatabai, A., Visharam, Z., Suzuki, T.: Compariosn of MCTF and closed-loop hierarchical B pictures. ISO/IEC JTC/SC29/WG11 and ITU-T SG16 Q.6, JVT-P059 (2005)
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Outline
• Overview• MCTF in JSVM• Scalability Concepts • JSVM Reference Software
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Outline
• Overview– Motivation– Scalable Video Coding
• MCTF in JSVM • Scalability Concepts• JSVM Reference Software
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Motivation
• To support clients with diverse capabilities in complexity, bandwidth, power, and display resolution.
Ethernet
Ethernet
Server
Wireless
Point-to-PointTransmission
Broadcasting
Router
Wireless
512 kbps
32 kbps
128 kbps
256 kbps
64 kbps
3 Mbps
1.5 Mbps
384 kbps
64 kbps
Bandwidth
Time
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Scalable Video Coding
• Approaches– wavelet-based
• 2D+t structure• t+2D structure
– AVC-based• Layered coding concept
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Approaches
A wavelet-based approach with 2D+t structure
Video
Entropy Coding
Motion Coding
Texture Coding
Spatio-Temporal "Transform"
2D Spatial DWT
Temporal TransformMCTF based
2D SpatialDecomposition
LL
Bitstream
In band TemporalTransform MCTF based
2D SpatialDecomposition
HF
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Approaches
A wavelet-based approach with t+2D structure
Video
Entropy Coding
Motion Coding
Texture Coding
Spatio-Temporal "Transform"
5/3 based MCTF 2D Spatial DWT
Bitstream
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Approaches
Spatio-Temporal "Transform"
Video
Entropy Coding
Motion Coding
Layered AVC TextureCoding
2D SpatialDecimation
QCIF
Bitstream
CIF
AVC 4x4 integer transform
AVC 4x4 integer transform
2D SpatialInterpolation
5/3 MCTF
5/3 MCTF
An AVC/H.264-based structure
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Scalabilities
• Temporal– fps
• Spatial– resolution
• SNR/Rate– quality
scheme Temporal
Spatial SNR/Rate
wavelet-based MCTF wavelet transform
(multi-resolution)
zero-tree coding
AVC-based MCTF Layered coding
CABAC (CGS)
Bit-plane coding (FGS)
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Outline
• Overview• MCTF in JSVM
– Why MCTF ?– Base layer structure– Inter layer prediction– Adaptive Prediction/Update Steps – Progressive MCTF
• Scalability Concepts• JSVM Reference Software
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Why MCTF?
• MCTF = Motion-Compensated Temporal Filtering
• A temporal sub-band coding– 2-channel filter bank in temporal
direction• Performs the wavelet decomposition /
reconstruction along the motion trajectory
• Implementation technique– Lifting scheme (the main reason) : Any
bi-orthogonal wavelet filters can be factorized by prediction and update steps
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Lifting scheme
• Attraction– An in-place implementation like
FFT.– Easy to build non-linear WT.– Insure PR.– All operations within one lifting
step can be done entirely parallel.
• Computational complexity– ~40% of original one (depend on
the wavelet filter)
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Lifting scheme
2
2
P USk
hk
lk
z-1
S2k+1
S2k
U P
2
2
Sk
S2k
S2k+1
z
Fh
Fl
Fh-1
Fl-1
Lifting Scheme(Analysis Filterbank)
(a)
Inverse Lifting Scheme(Synthesis Filterbank)
(b)
2
2
P USk
hk
lk
z-1
S2k+1
S2k
U P
2
2
Sk
S2k
S2k+1
z
Fh
Fl
Fh-1
Fl-1
Lifting Scheme(Analysis Filterbank)
(a)
Inverse Lifting Scheme(Synthesis Filterbank)
(b)
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Factoring Wavelet Transforms into Lifting Steps• 2-channel Filter
Bank
• Bi-orthogonal
2H0(z) 2 y[n]x[n] F0(z)
2H1(z) 2 F1(z)
2 2 y[n]x[n]
2 2
1H z
1G z
H z
G z
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Factoring Wavelet Transforms into Lifting Steps• PR condition
• Define
1 1
1 1
2
0
H z H z G z G z
H z H z G z G z
H z H zz
G z G z
M
1 2z z M M I
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Factoring Wavelet Transforms into Lifting Steps• Type 1 polyphase representation
• Define
2 1 2
2 1 2
e o
e o
H z H z z H z
G z G z z G z
e e
o o
H z G zz
H z G z
P
2 11
12
zz z
z
z z
P M
P P I
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Factoring Wavelet Transforms into Lifting Steps• Noble identities
H(zL) L H(z) L
H(z) MH(zM) M
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Factoring Wavelet Transforms into Lifting Steps• We want and are FIR.
• By Euclidean algorithm, we can get
zP zP
det z cz P det 1z assumption P
1
1 0 01
1 0 1/0 1
mi
i i
Ks zz
t z K
P
1
11
1 0 1 1/ 0
1 00 1
mi
ii
t z Kz
s z K
P
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Factoring Wavelet Transforms into Lifting Steps
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Wavelet filters
• 2-2 Filter Bank (Haar)
• 5-3 Filter Bank
2,2 2,2
11 1 1 1
2L H
2,2 2,20 0
11
2P U
5,3 5,33
1 11 2 6 2 1 1 2 1
2 2L H
5,3 5,30 0 2
1 11 1 1 1
2 2P U
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Lifting scheme
2
2
P USk
hk
lk
z-1
S2k+1
S2k
U P
2
2
Sk
S2k
S2k+1
z
Fh
Fl
Fh-1
Fl-1
Lifting Scheme(Analysis Filterbank)
(a)
Inverse Lifting Scheme(Synthesis Filterbank)
(b)
2
2
P USk
hk
lk
z-1
S2k+1
S2k
U P
2
2
Sk
S2k
S2k+1
z
Fh
Fl
Fh-1
Fl-1
Lifting Scheme(Analysis Filterbank)
(a)
Inverse Lifting Scheme(Synthesis Filterbank)
(b)
],[2
1,
]2,[2,
khkh
ksks
Haar
Haar
xxU
xxP
]1,[],[4
1,
]22,[]2,[2
12,
3/5
3/5
khkhkh
ksksks
xxxU
xxxP
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MCTF
H H H H
H 2 H 2
H 3
H H H H 1
H 2 H 2
H 3
L H 4
15Hz Video Sequence
7.5Hz Video Sequence
30Hz Video Sequence
3.25Hz Video Sequence
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MCTF
(a) Without M.C. (b) With M.C.
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Lifting scheme
2
2
P USk
hk
lk
z-1
S2k+1
S2k
U P
2
2
Sk
S2k
S2k+1
z
Fh
Fl
Fh-1
Fl-1
Lifting Scheme(Analysis Filterbank)
(a)
Inverse Lifting Scheme(Synthesis Filterbank)
(b)
2
2
P USk
hk
lk
z-1
S2k+1
S2k
U P
2
2
Sk
S2k
S2k+1
z
Fh
Fl
Fh-1
Fl-1
Lifting Scheme(Analysis Filterbank)
(a)
Inverse Lifting Scheme(Synthesis Filterbank)
(b)
0 0
0 0
5/3 0 0 1
5/3 0 0 1
, 2 1 ,2 2
1,2 , 2
21
,2 1 ,2 2 ,2 2 22
1,2 , , 1
4
Haar P P
Haar U U
P P P
U U U
P s k s k r
U s k h k r
P s k s k r s k r
U s k h k r h k r
x x m
x x m
x x m x
x x m x
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Base layer Structure
• Compatible with AVC Main profile– Dyadic hierarchical B pictures– Only prediction step is performed.
(UMCTF)
I0/P0 B1B2B3 I0/P0 I0/P0B3 B3 B3B3 B3 B3 B3B2 B2 B2B1
0 1221 8 167 9 153 5 11 136 10 144display order
group of pictures (GOP) group of pictures (GOP)
I0/P0 B1B2B3 I0/P0 I0/P0B3 B3 B3B3 B3 B3 B3B2 B2 B2B1
0 1221 8 167 9 153 5 11 136 10 144display order
group of pictures (GOP) group of pictures (GOP)
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Base layer Structure
• Non-dyadic decomposition is available– Temporal scalability
H1
L0
H2
L1
L0
H1
L0
L2
H3
L1
L0
H1
L0
H2
L1
L0
H1
L0
L2
H3
L1
L0
H1
L0
H2
L1
L0
H1
L0
L2
L3
L1
L0
H1H1
L0L0
H2H2
L1L1
L0L0
H1H1
L0L0
L2L2
H3H3
L1L1
L0L0
H1H1
L0L0
H2H2
L1L1
L0L0
H1H1
L0L0
L2L2
H3H3
L1L1
L0L0
H1H1
L0L0
H2H2
L1L1
L0L0
H1H1
L0L0
L2L2
L3L3
L1L1
L0L0
Level 0: full resolution
Level 1: 1/2 of the full resolution
Level 2: 1/4 of the full resolution
Level 3: 1/12 of the full resolution
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Inter Layer Prediction
• Remove the redundancy among the different layers– Residues– Motion vectors
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Inter Layer Prediction
Video Bitstream
2D Decimation(by 2)
MultiplexMCTF
Motion CodingMotion
TextureSpatial Transform -
SNR ScalableEntropy Coding
2D Decimation(by 4)
MCTF
Motion CodingMotion
Texture
MCTF
Motion CodingMotion
Texture
Prediction
Prediction
Prediction
Interpolation
Interpolation
Spatial Transform -SNR Scalable
Entropy Coding
Spatial Transform -SNR Scalable
Entropy Coding
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Adaptive Prediction/Update Steps• Goal
– Control the encoding delay
• Method– GOP is partitioned into sub-groups
• Restrictions : no across the partition boundary– Backward prediction steps– Backward and forward update steps
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Adaptive Prediction/Update Steps
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
H
0
H H H H H H H
L L L L L L L L
H H H H
L L L L
H H
L L
H
L
80 G 80 G30 C 30 C
1 0 2 4 6 5 7 3 9 8 10 12 14 13 15 11coding order
prediction
update
prediction
update
prediction
update
prediction
update
GOP border partition bordersub-partition
borderGOP border
sub-partition border
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Progressive MCTF
• Prediction steps and update steps are interlaced.
• Process the pictures in the reverse display order.
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Progressive MCTF
1 2 3 4 5 6 7 8
H
0
H H H
L L L L
H H
L L
H
L
prediction
update
prediction
update
prediction
update
GOP border GOP border
L´L´
L´
L´
12457813
6914 3
101115
1216
18
17
Numbering of prediction and update steps
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Outline
• Overview• OMCTF in JSVM • Scalability Concepts
– Three Scalabilities– Slice Types– Combined scalability
• JSVM Reference Software
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Temporal Scalability
L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0
H2L2 H2
L2 H2L2
H3
L3
H3
L1 H1L1 H1
L1 H1L1 H1
L1 H1L1 H1 {MP}1
{MP}2
{MP}3Temporal Enhancement Layer (Layer 1)
Temporal Base Layer (Layer 0)
Temporal Enhancement Layer (Layer 2)
Temporal Enhancement Layer (Layer 3)
L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0
H2H2L2L2 H2H2
L2L2 H2H2L2L2
H3H3
L3L3
H3H3
L1L1 H1H1L1L1 H1H1
L1L1 H1H1L1L1 H1H1
L1L1 H1H1L1L1 H1H1 {MP}1{MP}1
{MP}2{MP}2
{MP}3{MP}3Temporal Enhancement Layer (Layer 1)
Temporal Base Layer (Layer 0)
Temporal Enhancement Layer (Layer 2)
Temporal Enhancement Layer (Layer 3)
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Spatial Scalability
L0* L0* L0* L0* L0* L0* L0* L0* L0* L0* L0* L0*
L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1
L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0
Spatial Base Layer (Layer 0)
Spatial Enhancement Layer (Layer 1)
reconstructedsequence
reconstructedand upsam pledsequence
H1 H1 H1 H1 H1 L1 H1 H1 H1 H1 H1 H1
reconstructedsequence
temporalsubbandpictures
Spatial upsampling
Base Layer Prediction
Reconstruction
L0* L0* L0* L0* L0* L0* L0* L0* L0* L0* L0* L0*L0* L0* L0* L0* L0* L0* L0* L0* L0* L0* L0* L0*
L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1
L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0 L0
Spatial Base Layer (Layer 0)
Spatial Enhancement Layer (Layer 1)
reconstructedsequence
reconstructedand upsam pledsequence
H1 H1 H1 H1 H1 L1 H1 H1 H1 H1 H1 H1H1 H1 H1 H1 H1 L1 H1 H1 H1 H1 H1 H1H1 H1 H1 H1 H1 L1 H1 H1 H1 H1 H1 H1
reconstructedsequence
temporalsubbandpictures
Spatial upsampling
Base Layer Prediction
Reconstruction
Interpolation filter: {1,-5,20,20,-5,1}
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SNR Scalability
Transform,Scal. / Quant.
EntropyCoding
Inv. Scaling,Inv. Transform
MC / IntraPrediction
+
++
-
-
Transform,Scal. / Quant.
EntropyCoding
Inv. Scaling,Inv. Transform
+ -
Transform,Scal. / Quant.
EntropyCoding
SNR Base Layer (Layer 0)
SNR Enhancement Layer (Layer 1)
SNR Enhancement Layer (Layer N-1)
General
Decomposition
of a Group of
Pictures using
Motion-Comp.
Temporal
Filtering
(MCTF) ...
Group of Pictures
.
.
.
Tem
pora
l su
bban
d P
ictu
res
.
.
.
Transform,Scal. / Quant.
EntropyCoding
Inv. Scaling,Inv. Transform
MC / IntraPrediction
+
++
-
-
Transform,Scal. / Quant.
EntropyCoding
Inv. Scaling,Inv. Transform
+ -
Transform,Scal. / Quant.
EntropyCoding
SNR Base Layer (Layer 0)
SNR Enhancement Layer (Layer 1)
SNR Enhancement Layer (Layer N-1)
General
Decomposition
of a Group of
Pictures using
Motion-Comp.
Temporal
Filtering
(MCTF) ...
Group of Pictures
.
.
.
Tem
pora
l su
bban
d P
ictu
res
.
.
.
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Slice Types
SliceType
Supported macroblock modes
INTRA_4x4 INTRA_16x16 INTRA_PCM INTRA_BASE RESIDUAL motion-compensated modes
M X(1) X
I X X X
P X X X X
B X X X X
IE X X X X
PE X X X X X
BE X X X X X
E X
H X X X X(2)
HE X X X X X(2)
(1) For M slices, the intra mode is called INTRA and it is not identical to the INTRA_4x4 mode.(2) The residual mode (RESIDUAL) is not indicated by the syntax element mb_type, instead the macroblocks that
are coded in residual mode are specified by the corresponding prediction data array.
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Slice TypesSlice Type Usage
M Coding of prediction data arrays
I
Coding of base-layer (SNR, spatial) representations of low-pass picturesP
B
IE
Coding of enhancement-layer (SNR, spatial) representations of low-pass picturesPE
BE
ECoding of SNR enhancement-layer representations of high-pass picturesCoding of enhancement-layer (SNR, spatial) representations of low-pass pictures
H Coding of base-layer (SNR, spatial) representations of high-pass pictures
HE Coding of spatial enhancement-layer representations of high-pass pictures
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Combined Scalability
H22 H0
0 H12 H0
0 L22 H0
0 H12 H0
0 H22 H0
0 H12 H0
0
I B P B P B
H20 H1
0 L20 H1
0 H20 H1
0
Spatial upsampling
H21 H1
1 L21 H1
1 H21 H1
1
H23 H0
1 H13 H0
1 L23 H0
1 H13 H0
1 H23 H0
1 H13 H0
1
{MP}1,2
{MP}0
Layer 0: QCIF, 7.5 Hz, 64 kbit/s
Layer 1: QCIF, 15 Hz, 128 kbit/s
Layer 2: CIF, 15 Hz, 256 kbit/s
Layer 3: CIF, 15 Hz, 512 kbit/s
Layer 4: CIF, 30 Hz, 1024 kbit/s
Layer 5: CIF, 30 Hz, 2048 kbit/s
H22 H0
0 H12 H0
0 L22 H0
0 H12 H0
0 H22 H0
0 H12 H0
0H22 H0
0 H12 H0
0 L22 H0
0 H12 H0
0 H22 H0
0 H12 H0
0H22 H0
0 H12 H0
0 L22 H0
0 H12 H0
0 H22 H0
0 H12 H0
0
I B P B P BI B P B P B
H20 H1
0 L20 H1
0 H20 H1
0H20 H1
0 L20 H1
0 H20 H1
0
Spatial upsampling
H21 H1
1 L21 H1
1 H21 H1
1H21 H1
1 L21 H1
1 H21 H1
1
H23 H0
1 H13 H0
1 L23 H0
1 H13 H0
1 H23 H0
1 H13 H0
1H23 H0
1 H13 H0
1 L23 H0
1 H13 H0
1 H23 H0
1 H13 H0
1H23 H0
1 H13 H0
1 L23 H0
1 H13 H0
1 H23 H0
1 H13 H0
1
{MP}1,2{MP}1,2
{MP}0{MP}0
Layer 0: QCIF, 7.5 Hz, 64 kbit/s
Layer 1: QCIF, 15 Hz, 128 kbit/s
Layer 2: CIF, 15 Hz, 256 kbit/s
Layer 3: CIF, 15 Hz, 512 kbit/s
Layer 4: CIF, 30 Hz, 1024 kbit/s
Layer 5: CIF, 30 Hz, 2048 kbit/s
2660/03/30 hclin 41
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Outline
• Overview• OMCTF in JSVM • Scalability Concepts• JSVM Reference Software
– Tools– UMCTF at Decoder
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ics, Natio
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Un
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Tools
• Converter – Spatial domain
• Upsample– Interpolation FIR filter
• Downsample– Apply an anti-aliasing FIR filter proir to
2D downsampling
– Temporal domain
• PSNR
2660/03/30 hclin 43
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UMCTF at Decoder
• Update step– Improve coding efficiency– Increase significantly complexity
of the decoder operation• Additional M.C. operations• Picture buffer management• M.V. needs intensive branch
instructions
2660/03/30 hclin 44
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ics, Natio
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UMCTF at Decoder
• UMCTF => update step at decoder side is omitted– The visual quality and PSNR of the
decoded video is not degraded– UMCTF → purely predictive
structure– Reduce the complexity of decoder
by 50%
2660/03/30 hclin 45
Institu
te of E
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ics, Natio
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Un
iversity
Normal Mode
STOCKHOLM
34.00000
34.50000
35.00000
35.50000
36.00000
1 4 7 10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
Stockholm_DEC_U_ON
Stockholm_DEC_U_OFF
CREW
35.50000
36.00000
36.50000
37.00000
37.50000
38.00000
38.50000
39.00000
39.50000
40.00000
1 4 7 10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
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61
64
Crew_Dec_U_ON
Crew_Dec_U_OFF
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iversity
High Quality (Qp = 0)
40
45
50
55
60
65
70
75
80
85
901 4 7 10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
Stockholm_QP0_UpdateDec_ON Stockholm_QP0_UpdateDec_OFF
50
55
60
65
70
75
80
1 4 7 10
13
16
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22
25
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31
34
37
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43
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61
64
Crew _QP0_UpdateDec_ON Crew _QP0_UpdateDec_OFF
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ics, Natio
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Un
iversity
Qp = 24
35
36
37
38
39
40
41
42
431 4 7 10
13
16
19
22
25
28
31
34
37
40
43
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64
Stockholm_QP24_UpdateDec_ON Stockholm_QP24_UpdateDec_OFF
39
40
41
42
43
44
45
1 4 7 10
13
16
19
22
25
28
31
34
37
40
43
46
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61
64
Crew _QP24_UpdateDec_ON Crew _QP24_UpdateDec_OFF