Vision friendly video coding towards
new standard H.265/HPVC
Manoranjan Paul
Research Fellow
School of Computer Engineering, Nanyang Technological University
Faculty Member, Charles Sturt University, Australia (from January 2011)
25th December 2010 at AUST, Bangladesh
http://sites.google.com/site/manoranjanpaulpersonalweb/
© 2010 Manoranjan Paul. All Rights Reserved.
Outline
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Personal Information in Brief
Video Compression and Video Coding
Video/Image Quality Assessment
Computer Vision & Video Coding
Eye Tracking Technology & Visual Attention
Abnormal Event Detections
New Research Areas
Conclusions
© 2010 Manoranjan Paul. All Rights Reserved.
Other people involved in the
Research Works
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Prof. Michael Frater, UNSW
Prof. John Arnold, UNSW
Prof. Laurence Dooley, Monash
A/Prof. Manzur Murshed, Monash
A/Prof. Weisi Lin, NTU
A/Prof. Chiew Tong Lau, NTU
A/Prof. Bu- Sung Lee, NTU
Dr. Chenwei Deng, NTU
Dr. Mahfuzul Haque, Monash
Dr. Golam Sorwar, SCU
Dr. Fan Zhang
Anmin Liu, NTU
Zhouye Gu, NTU
© 2010 Manoranjan Paul. All Rights Reserved.
Personal Information
• Education:
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PhD, Monash University (2005). Thesis title: Block-based very low bit rate video coding
techniques using pattern templates. -nominated for Mollie Holman Gold Medal, 2006.
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Bachelor of Computer Science and Engineering (4 years honors degree with research
project), Bangladesh University of Engineering and Technology, Dhaka, Bangladesh (1997). Thesis
Supervisor: Professor Chowdhury Mofizur Rahman
• Employments:
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Current Position:
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Faculty Member, Charles Sturt University, Australia (From January 2011)
Research Fellow, Nanyang Technological University, Singapore (World Rank 69), CI:
Professor Weisi Lin.
Previous Positions:
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Research Fellow and Lecturer (05/06~03/ 09), Monash University (World Rank 45), Under
ARC Discovery Grant, CI: Professor Manzur Murshed.
Research Fellow, Under an ARC DP project, ADFA, The University of New South Wales
(World Rank 47), CI: Professor Michel Frater.
Assistant Lecturer (11/ 01~04/05), Monash University, Australia.
Assistant Professor (10/00~10/01), Ahsanullah University of Science and Technology,
Bangladesh.
Lecturer (09/97~09/00), Ahsanullah University of Science and Technology, Bangladesh.
© 2010 Manoranjan Paul. All Rights Reserved.
Personal Information Cont…
• Research:
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Publish 50+ International articles
Deliver keynote speech in the IEEE ICCIT conference, 2010
Organize a special session on the IEEE ISCAS 2010 on “Video coding”
Supervise 5 PhD students and examine 5 PhD and MS theses
Editor of (i) International Journal of Engineering and Industries (IJEI) and
(ii) Special Issues (2008, ‘09, ‘10, & ‘11) of Journal of Multimedia.
– Served Program Committee member for 15 International Conferences.
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Research Quality:
– Publish 7 IEEE Transactions Papers in TIP(3), TCSVT(3), and TMM (1)
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IEEE TIP: IF: 4.6, Top rank journal in Image Processing, JCR Rank: Q1
IEEE TCSVT: IF: 4.3, Top ranked journal in Video Technology, JCR Rank: Q1
IEEE TMM: IF: 2.9, Top ranked journal in Multimedia, JCR Rank: Q1
Publish 25+ IEEE Conf. e.g., ICIP(6), ICASSP(4), MMSP(3), ICPR(1), ISCAS(1)
An ARC DP grant awarded from my PhD works
Obtained $80,000 Competitive grant money
Involve reviewing of IEEE TIP, IEEE TCSVT, IEEE TMM, IEEE SPL, IEEE CL.
Publications H-index is 7 (according to Google)
© 2010 Manoranjan Paul. All Rights Reserved.
Outline
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Personal Information in Brief
Video Compression and Video Coding
Video/image Quality Assessment
Computer Vision & Video Coding
Eye Tracking Technology & Visual Attention
Abnormal Event Detections
New Research Areas
Conclusions
© 2010 Manoranjan Paul. All Rights Reserved.
Why Video Compression and
Video Coding
Original ICE video sequence
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Frame 46
Frame 61
A number of frames (comprises video) going through faster in front of us
provides moving picture impression i.e., video
One second video requires = 25×288×352×8×3 bits = 59,400 kilo bits
Whereas YouTube supports only 384 kilo bits per second
Thus, we need to compress the video data by 200 times
To compress data we need Video Coding
Video Coding Standards: Motion JPEG, MPEG-2, MPEG-4, H.264, H.265
© 2010 Manoranjan Paul. All Rights Reserved.
Video Coding Steps (H.264)
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First Frame of Tennis video
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Frame Difference
16
10 11 12 13 14 15 16 17 18 19 20 21 22
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16
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Macroblock
15
2d+1+M
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Frame encoding format is IPPP…,
IBBP…, or IBPBP…
Processed P-frame using small block,
Macroblock, 16×16 pixel block
For more compression try to match
each block into the already coded
frames, Motion Estimation
Further divided the 16×16 block into
16×8, 8×16, 8×8, 8×4, 4×8, and
4×4
Then select the best Mode using bit
rates (R), distortions (D), and
Lagrangian parameter (λ)
(k+u, l+v)
X
Y
Motion Vector
(u,v)M(otion
d
M
M
2d+1+M
(k,l)
MB of Reference
frame
J  D(mi )  R(mi )
MB of Current
frame
Figure: BlockdMatching
Technique
Search Window of Reference Frame
© 2010 Manoranjan Paul. All Rights Reserved.
Limitations of Variable Block Size ME
and MC
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10 11
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12 13 14 15 16 17 18 19 20 21 22
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88
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A Frame or Picture of a video
16  16 pixels block
A Frame or picture divided into
16×16 pixel blocks for coding
• Each block may have different
motions thus, 16×16, to 4×4
blocks are used to estimate motion
Observations
• At low bit rate the largest block (i.e.,
16×16) is selected more than
60~70%.
• At the higher bit rate 16×16 is
20~30 % but smallest block 8×8)
40~50%.
• The variable-block size ME & MC
of H.264 is not effective, as
expected, at Low Bit Rates
(a) Frame #2 of Miss America sequence, (b) different size blocks,
(c) & (d) different size blocks of Frame #2 at low and high bit rate respectively.
© 2010 Manoranjan Paul. All Rights Reserved.
Exploitation of Intra-Block Temporal
Redundancy by Pattern Templates
Fig. An example on how pattern-based coding can exploit the intra-block temporal
redundancy in improving coding efficiency
© 2010 Manoranjan Paul. All Rights Reserved.
Pattern-based video coding at LBR
Published in IEEE Transaction on Image Processing 2010 and IEEE TCSVT 2005
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A set of pre-defined pattern templates
A block has foreground and background
Pattern matching can separates them
Encoding foreground and skipping background
provides compression
Motion estimation using only moving regions also
provides computational efficiency
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Observation:
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Pattern-matching
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The PVC provides 1.0~2.0dB PSNR
improvement or 10~20% more
compression compared to the H.264 at
LBR
The PVC provides 20~30% computational
reductions compared to the H.264
© 2010 Manoranjan Paul. All Rights Reserved.
Arbitrary-Shaped Pattern Templates
published in IEEE SPL 2007
Moving regions distributions
The final pattern template
Pattern templates from
different video sequences
Clustering the moving regions
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Highest magnitude 64 pixels positions
Rate-distortion performance comparison
Pre-defined pattern templates sometimes cannot
approximate the object shapes properly
Arbitrary-shaped pattern templates generated from video
can better approximate the object shapes, thus provides
better coding performance
ASPVC provides 0.5dB PSNR or 5% more compression
compared the PVC
© 2010 Manoranjan Paul. All Rights Reserved.
Singular Value Decomposition (SVD)
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DCT VS. SVD
DCT
Original Image
DCT Coefficients
Coefficient distribution of the120th frame after DCT
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2D-SVD
Original Image
2D-SVD Coefficients
Coefficient distribution of the 120th frame after SVD
2D-SVD yields fewer high amplitude
coefficients (dark spots in the coefficient matrix)
Hybrid Video Codec (MPEG-1, 2, -4, H.263/4)
Advantage: Exploits temporal
redundancy by motion estimation,
high compression
Disadvantage: High
computational complexity, Interframe dependency, not support
random frame access function,
error propagation
Intra-frame Video Codec
(Motion JPEG, Motion
JPEG2000)
Advantage: Low computational
complexity, Inter-frame
independency, Support random
access to each frame, Facilitate
video editing, no error
propagation
Disadvantage: Low compression
© 2010 Manoranjan Paul. All Rights Reserved.
The Framework of SVD Video Codec
Published in IEEE ICIP 2010
Input Video sequence
Ai
Frames subtracted by
meanframe
Frame
Normalization
Normalized frames
16 x 16
MB
group Bi
A i'
i=1...n
i=1...n
Coefficient matrix
group
Normalization
Coefficients
Ai'
16x16
coefficient
matrix
group Mi
2D-SVD
F
i=1...n
Mean Frame
substraction
Mean frame
eigenvector
matrix U li U ri
of every MB Bi
Amean
Quantization &
Entropy Coding
JPEG
Compression
Group Information Headfile1
Operations
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Coefficient matrix of
frame 1
Headfile2
Coefficient matrix of
frame 2
Headfile
3
Coefficient matrix of
frame 3
…...
Operation
Result
SVD is 100 times faster than the H.264 video coding standard
Suitable for mobile video phone & surveillance video coding
Image quality gain 5.0dB compared to Motion J2K
Better for random access and noisy transmission
© 2010 Manoranjan Paul. All Rights Reserved.
Super-High Definition (4096×2160) Video Coding
Published in IEEE ICIP 2010
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PSNR (dB)
PSNR (dB)
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50
H.264 FRExt
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BS+SR+SKIP
BS+SR
BS
Standard H.264 FRExt
Motion JPEG2000
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Motion JPEG2000
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0
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Bit per pixel (bpp)
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PSNR (dB)
PSNR (dB)
50
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H.264 FRExt
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Motion JPEG2000
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bpp
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Bit per pixel (bpp)
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H.264 vs. JPEG
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• Answer: Yes, if
45
BS+SR+SKIP
BS+SR
BS
Standard H.264 FRExt
Motion JPEG2000
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26
5
• H.264 outperforms
the JPEG 2000?
• Answer: No
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0
1
2
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bpp
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• Increase block size to
32×32 instead of
16×16
• Increase search range
to 128 instead of 32
• Extend Skip block
definition to 32×32
instead of 16×16
H.264* vs. JPEG
© 2010 Manoranjan Paul. All Rights Reserved.
Optimal Compression Plane for Video Coding
Accepted in IEEE ICIP 2010 and IEEE Transactions on Image Processing 2010*
adaptive compression plane
transform for each PPU
T
X
Y
TX
determine
optimal
compression
plane
source determine
video PPU size
video
coded
coding
bits
standards
XY
TY
Figure: Different Compression Planes such as XY, TX, and TY
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32
32
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24
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0.5
1
bpp
1.5
28
XY
OCP(N=32)
OCP(N=128)
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2
0
28
24
20
24
XY
OCP(N=32)
OCP(N=128)
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psnr
psnr
psnr
32
0.5
1
bpp
1.5
XY
OCP
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12
2
0
0.5
1
bpp
Figure: Rate-distortion performance using JPEG 2000 and H.264 for Mobile, Tempete, and Tempete video sequences (left to right)
© 2010 Manoranjan Paul. All Rights Reserved.
1.5
2
Occlusion Handling using Multiple
Reference Frames (MRFs)
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Frame 1
To encode current frame, a video encoder uses previously encoded multiple
frames for the best matched frame
The MRFs (using up to 16 frames in the H.264) technique outperforms the
technique of using only one reference frame when:
• Repetitive motion
• Uncovered background
• Non-integer pixel displacement
• Lighting change, etc.
Frame 2
Motion estimation and
compensation
Frame 3
Frame 6
Frame 4
Frame 5
Silent video sequence
Frame 18
Uncovered
background
Frame 18
Light changed
Disadvantages
– Required k (number of reference frames) times computations
– Required k times more memory in encoder and decoder
– No improvement if relevant features are missing
What happens if the cycle of features exceeds the k reference frames
© 2010 Manoranjan Paul. All Rights Reserved.
Frame 30
Repetitive motion
Dual Reference Frames: A Sub Set of MRFs
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Dual (short term and long term) reference frames technique instead of
MRFs technique gains popularity instead of MRFs
Short term reference frame (STR): Immediate previous frame for
foreground.
Long term reference (LTR) frame for stable background.
• One of the previously coded frame is selected for LTR
• Selecting and updating LTR frame is complicated
• Cannot capture uncovered background for the most of the time
• Cannot capture stable background
• May not be effective for implicit background and foreground
referencing
To capture uncovered background, repetitive motions, etc. we need a
ground truth background
but in the changing environment it is almost impossible to get the
background of a video
thus, we need to model a Most Common Frame of A Scene (MCFIS)
using video frames.
McFIS: The Most Common Frame of a Scene
published in IEEE ICASSP-10, ICIP-10, ISCAS-10
Observation:
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A true background can capture a major portion of a scene
Difference between the current frame and background provides object
Coding only foreground provides coding efficiency
Original ICE video sequence
Background Generation
© 2010 Manoranjan Paul. All Rights Reserved.
McFIS for Background Areas
Accepted in IEEE Transactions on Image Processing 2010* & ICASSP-10
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Instead of MRFs, a McFIS is enough
A McFIS enables the possibility of capturing a
whole cycle of features
The immediate previous frame is for moving
areas and the McFIS is for background regions.
Less computation (60% saving) in ME&MC is
required using McFIS
Improvement around 1.0dB PSNR compared
to the existing methods
Implicit foreground (STR) and background (LTR, i.e.,
McFIS) referencing where black regions are
referenced from the STR and other regions are
referenced using McFIS
© 2010 Manoranjan Paul. All Rights Reserved.
Rate-distortion performance
© 2010 Manoranjan Paul. All Rights Reserved.
Scene Change Detection by McFIS
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Two mixture video sequences created
A simple SCD detection algorithm using the McFIS
has been developed (i.e., SAD between McFIS and
Current frame)
Compare our results with Ding et al. IET IP 2008
The proposed SCD algorithm is better
© 2010 Manoranjan Paul. All Rights Reserved.
Adaptive GOP Determination by SCD
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I-frame requires two to three times more bits than P or B-frame
if a sequence does not contain any scene changes or extremely high motion
activity, insertion of I-frames reduces the coding performance
Optimal I-frame insertion using adaptive GOP and SCD improves coding
performance
We have compared our results with other two latest algorithms
The proposed AGOP method outperforms existing methods
NUMBER OF I-FRAMES FOR MIXED VIDEO A AND B OF 700 FRAMES
Number of I-frames
Mixed Video A
Mixed Video B
Methods
QPs
SCD
AGOP
SCD
AGOP
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10
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Proposed
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Algorithm
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40
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Ding’s
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Algorithm
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0
10
4
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Matsuoka’s
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Algorithm
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© 2010 Manoranjan Paul. All Rights Reserved.
McFIS as an I-frame
Accepted in IEEE Trans. on Cir. & Sys. for Video Technology 2010* & IEEE ISCAS-10
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A frame being the first frame is not the best
I-frame
An ideal I-frame should have the best
similarity with other frames, thus better for
P/B-frames in terms of bits and image
quality
Insertion of more than one I-frame within a
scene degrades the coding performance
Conventional Frame Format
Proposed
More background area using McFIS
Computational Time Reductions
© 2010 Manoranjan Paul. All Rights Reserved.
McFIS for Reducing Quality Fluctuations
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Fluctuation of PSNRs
Rate-Distortion performance
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Fluctuation of Bits per frame
Rate-Distortion performance
© 2010 Manoranjan Paul. All Rights Reserved.
Better I-frames are
generated for
coding efficiency
More consistent bit
per frame and
image quality by
the proposed
method
The proposed
method improved
1.5dB PSNR
Better for Scene
Change Detection
Coded Videos using Different Schemes
Original Video
© 2010 Manoranjan Paul. All Rights Reserved.
H.264 Mode Selection by Distortion Only
published in IEEE Transactions on Multimedia 2009
H.264 mode selection
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J LM  D    ( RMV  RH  RDCT )
mn  arg min ( J LM (mi )) R(mi )  RT
mi
New H.264 mode selection
J Dist  D  m  ( RMV  RH )
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The H.264 supports a number of modes
such as intra, skip, direct, 16×16 16×8,
8×16, 8×8, 8×4, 4×8, and 4×4 to encode
a 16×16 block
The H.264 requires around 10 time more
computational times compared to the H.263
Thus, limited powered devices (mobile
phone, PDA, etc.) can not use H.264
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Mode selection using only bits from
motion vectors and headers with
entropy bits provides 12% reductions
of computational time
Modified Lagrangian Multiplier is also
derived
Only 0.1dB PSNR may be degraded
© 2010 Manoranjan Paul. All Rights Reserved.
Mode Selection Using Phase Correlation
Different peaks for different kinds of motions
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Relationship of motion inconsistence metric, δ
with object movements
Direct Mode Selection from Binary Matrix
Phase correlation provides relative motion between
two blocks
To direct mode selection we exploit this object motion
For example,
o one big peak-> no motion
o One small peak-> single motion
o Two peaks-> multiple motions
© 2010 Manoranjan Paul. All Rights Reserved.
Direct Mode Selection for Efficient Coding
published in IEEE Transactions on IP 2010
Mode Selection by the H.264
Mode Selection by the our method
Rate-Distortion performance sometimes better compared to the H.264
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Time saving 60~90% compared to H.264
Rate-distortion performance is comparable to the H.264
Can be applied to other fast mode selection schemes
Time saving 60~90% compared to H.264
© 2010 Manoranjan Paul. All Rights Reserved.
Outline
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Personal Information in Brief
Video Compression and Video Coding
Video/Image Quality Assessment
Computer Vision & Video Coding
Eye Tracking Technology & Visual Attention
Abnormal Event Detection
New Research Areas
Conclusions
© 2010 Manoranjan Paul. All Rights Reserved.
Video/Image Quality Assessment
published in IEEE ICME 2010 and IEEE Transactions on CSVT 2010
Figure: Block diagram of pixel domain Just Noticeable Distortion (JND) model
Figure: Image Decomposition; (a) Original Barbara Image, (b) Structural part, and (c) Texture part
© 2010 Manoranjan Paul. All Rights Reserved.
Outline
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Personal Information in Brief
Video Compression and Video Coding
Video/Image Quality Assessment
Computer Vision & Video Coding
Eye Tracking Technology & Visual Attention
Abnormal Event Detections
New Research Areas
Conclusions
© 2010 Manoranjan Paul. All Rights Reserved.
Environment (objects, background,
shadow, etc.) Modeling
How to extract the active regions from surveillance video stream?
Background Subtraction
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Current frame
=
Background
Moving foreground
Challenges!!
• Background initialization is not a practical approach in real-world
• Dynamic nature of background environment due to illumination
variation, local motion, camera displacement and shadow
© 2010 Manoranjan Paul. All Rights Reserved.
Modeling using Gaussian Mixtures
2
σ
P(x)
µ
P(x)
Sky
Road
Floor
Cloud
Shadow
Shadow
Leaf
Moving Car Walking
Moving Person
People
x
2
σ
Cloud
µ
x
P(x)
Person
Leaf
Sky
P(x)
2
σ
µ
x (Pixel intensity)
x
© 2010 Manoranjan Paul. All Rights Reserved.
Moving Object Detection
Frame 1
Frame N
road
shadow
car
road
shadow
Models are ordered by ω/σ
K models
ω12
σ1
µ1
road
ω22
σ2
µ2
ω32
σ3
µ3
car
shadow
65%
Background Models
Learning Rate α
15%
20%
 b

B  argminb   ωk  T 
 k 1

T is minimum portion of data in the environment accounted for background.
Matched model for a new pixel value Xt, |Xt - µ | < T
σ
T = 70%
*σ
© 2010 Manoranjan Paul. All Rights Reserved.
Dynamic Background Modeling
published in IEEE ICPR-08, AVSS-8, and MMSP-08
Observation:
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Predefined background/foreground ratio does not work for all cases
Background generation using mean makes tailing effect
Unnecessary background models using existing modeling
Contributions:
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Make environment independent to work in any environment
Provide emphasis on recent changes to avoid tailing effect
New criteria to avoid multiple models for the same environment component
Original ICE video sequence
Background Generation
© 2010 Manoranjan Paul. All Rights Reserved.
Object Detections - 1
First
Frame
Test
Frame
Ground
Truth
Sfm
Lf
(1)
(2)
(3)
(4)
(5)
(1) PETS2000; (2) PETS2006-B1; (3) PETS2006-B2; (4) PETS2006-B3; and (5) PETS2006-B4.
© 2010 Manoranjan Paul. All Rights Reserved.
Pf
Object Detections - 2
First
Frame
Test
Frame
Ground
Truth
Sfm
Lf
Pf
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(6) Bootstrap; (7) Camouflage; (8) Foreground Aperture; (9) Light Switch; (10) Moved Object; (11) Time Of Day; and (12) Waving Tree
© 2010 Manoranjan Paul. All Rights Reserved.
Object Detections - 3
First
Frame
Test
Frame
Ground
Truth
Sfm
Lf
(13)
(14)
(13) Football; and (14) Walk
© 2010 Manoranjan Paul. All Rights Reserved.
Pf
Outline
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Personal Information in Brief
Video Compression and Video Coding
Video/Image Quality Assessment
Computer Vision &Video Coding
Eye Tracking Technology & Visual Attention
Abnormal Event Detections
New Research Areas
Conclusions
© 2010 Manoranjan Paul. All Rights Reserved.
Eye Tracking Technology & Visual Attention
Recent Research
Figure: Three Types of Eye trackers
Figure: Different heat maps for normal
(left) and abnormal driving (right)
Figure: Original image, eye movement and fixation when observers asked for free observations, when asked to
determine age of the people, when asked to determine people positions (left to right picture).
Figure: Heat map using eye tracker (left) and salience map (right)
© 2010 Manoranjan Paul. All Rights Reserved.
Outline
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Personal Information in Brief
Video Compression and Video Coding
Video/Image Quality Assessment
Computer Vision & Video Coding
Eye Tracking Technology & Visual Attention
Abnormal Event Detections
New Research Areas
Conclusions
© 2010 Manoranjan Paul. All Rights Reserved.
On Going Project
Abnormal event detection using vision friendly video coding
This project contributes on:
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Interactive 3D Video Technology
Eye Tracking technology
Visual attention modeling
Abnormal event detection
© 2010 Manoranjan Paul. All Rights Reserved.
New Research Areas
• 3D Video
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3D Video Coding
Multi-view Video Coding
Depth Estimation
Free-View Videos
Distributed Video Coding
© 2010 Manoranjan Paul. All Rights Reserved.
Conclusions
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Improve compression and video quality
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Pattern-based (regular and arbitrary-shaped) video coding
Super-High Definition (SHD) video coding
Video coding for uncovered background and occlusions
Optimal compression plane (OCP) selection
Singular Value Decomposition-based video coding
Reduce computational complexity in video coding
– Phase correlation-based direct mode selection
– Simplified Lagrangian function for low-cost mode selection
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Image Decomposition and Quality Assessment
– Separating structure and texture areas of an image
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Background modeling & Object Detection
– Object detection from the challenging environments
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Panic-driven event detection
– Event detection using low-level features
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Eye tracking technology & visual attention modeling
– Human-centric regions of interest selection
© 2010 Manoranjan Paul. All Rights Reserved.
Thank you!
Email: [email protected]
http://sites.google.com/site/manoranjanpaulpersonalweb/
© 2010 Manoranjan Paul. All Rights Reserved.