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Logarithmic Discrete Wavelet Transform for High Quality Medical Image Compression
By: Mohammed IBRAHEEM
29-03-2017
Prof. YANG-SONG Fan Reviewer
Prof. RABAH Hassan Reviewer
Prof. BENSRHAIR Abdelaziz Examiner
Prof. LEMIRE Daniel Examiner
Dr. HACHICHA Khalil Supervisor
M. HOCHBERG Sylvain Supervisor
Prof. GARDA Patrick Supervisor
Jury Members
Prof. MEHREZ Habib President
Outlines
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 33
IntroductionPart I
State of the artPart II
Logarithmic Library for image processingPart III
Logarithmic DWT based Compression Part IV
2D-DWT Hardware ArchitecturePart V
Conclusion and Future workPart VI
Part IIntroductionIntroduction
29-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 4
ContextContext
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 55
E-health systemsMedical imaging technology growthResolution/Image size
E-health systemsMedical imaging technology growthResolution/Image size
Needs ?
Archive Remote access Embedded solutions
Limitations ?
Storage cost → huge numbers dailyo Full MRI exam can produce 10 GB
Bandwidth limited resources on embedded systems
ChallengesChallenges
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 66
Trade-off between image quality/compression
Vital information → preserving quality → avoid misdiagnoses
Q: How to achieve an efficient image compression while preserving the diagnostic quality?
First Challenge
Second Challenge Speed
Q: How to achieve a high-speed compression on Embedded systems?
The compression algorithm on embedded systems/limited resources
Time is a life saver
Part IIState of the artState of the art
29-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 7
Original Image
CompressedImage
Original Image
CompressedImage
Original Image
CompressedImage
Image Compression Algorithms Used in Medical domain Image Compression Algorithms Used in Medical domain
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 88
JPEG
Quant.DCTEntropyEncoder
Color Space
Transform
Quant.DWTEntropyEncoder
Color Space
Transform
Quant.DWTHENUCEncoder
Color Space
Transform
Based on DCT → Block artifacts
JPEG2000
Two compression modes: lossy / lossless Based on DWT No block artifacts
WAAVES
Two compression modes: lossy / lossless Based on DWT → No block artifacts Medical certified → clinical tests Efficient encoder
Hierarchical Enumerative Coding
Image Quality Issues in Image CompressionImage Quality Issues in Image Compression
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 99
Quality
issues
Arithmetic
(Real numbers)
DWT/DCT
Quantization
(Division + rounding)
Arithmetic alternatives in state of the art
• Floating-point
• Fixed-pointo Limited accuracy
o High speed - HW
Simple div/mul operations
Accuracy near to FLP
An alternative to FLP on embedded systems
Accelerate the DSP apps
Compressed
ImageQuant.
DWT
DCTEncoder
Color
Space
Transform
Original
Image
Multiplication 𝒍𝒐𝒈𝟐 𝒙 × 𝒚 = 𝒂 + 𝒃
Division 𝒍𝒐𝒈𝟐 𝒙 ÷ 𝒚 = 𝒂 − 𝒃
Addition 𝒍𝒐𝒈𝟐 𝒙 + 𝒚 = 𝒃 + 𝒍𝒐𝒈𝟐(𝟐𝒂−𝒃 + 𝟏)
Subtraction 𝒍𝒐𝒈𝟐 𝒙 − 𝒚 = 𝒃 + 𝒍𝒐𝒈𝟐(𝟐𝒂−𝒃 − 𝟏)
a= 𝒍𝒐𝒈𝟐 𝒙 , b= 𝒍𝒐𝒈𝟐 𝒚
No existing research addressed it in the image compression domain
Recently: Logarithmic number system (LNS)
Application : Smart-EEG Project (New tool) Application : Smart-EEG Project (New tool)
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1010
Mentoring RequirementsMentoring Requirements
Fast myoclonus jerks high frame rate → 100 f/s
• Currently: 30 f/s
Why 100 f/s ?Why 100 f/s ?
Correct diagnosis Real time constraints
Exam procedureExam procedure
EEG acquisition
Video acquisition
Video compression
Sync. + Transmission
Camera
(Video acquisition)
EEG Cap
(acquisition)
Academic
Industry
Hospitals
Compression Block on Smart-EEG Compression Block on Smart-EEG
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1111
HENUC Encoder unit→ implemented in LIP6 by:
Yhui / Zahid / Laurent
DWT unit → required for integration → full compression chain
Many DWT solutions
• SW : GPU/DSP
• HW: ASIC/FPGA
High speed
Limitations
DDR RAM latency not addressed
The lack of memory optimization and compatibility with HENUC
DWT Related work
Compression algorithm choice: WAAVES
Problem Statement Problem Statement
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1212
Does the logarithmic representation has the ability to improve
the trade-off between the compression ratio and the image quality?
Q.1
How to provide a new DWT hardware architecture that can
fulfill the Smart-EEG high-speed requirement?
Q.2
Part IIILNS Library For Image Compression LNS Library For Image Compression
29-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 13
IntroductionIntroduction
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1414
ObjectivesObjectives
The need of a tool to study the logarithmic domain Image compression compatibility
IssuesIssues
The logarithm of a negative number is undefined The logarithm of zero is undefined log(0) = -∞ The quantization process
Sign AmbiguitySign Ambiguity
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1515
Logarithmicdomain
Lineardomain
𝒙= -16𝒙= -16 𝒙 = 16
𝒗 = 𝒍𝒐𝒈𝟐( 𝒙 )
𝒗 = 𝟒
𝒙 = 𝟐𝒗
𝒙 = 𝟏𝟔
Lineardomain
Logarithmicdomain
Lineardomain
𝒙= -16𝒙= -16 𝒙 = 16
𝒗 = 𝒍𝒐𝒈𝟐( 𝒙 )
𝒙 = −𝟏𝒔 × 𝟐𝒗
Lineardomain
𝒔 = 𝟏 𝒔 = 𝟎
𝟎 4𝒔 𝒗
𝟏 4𝒔 𝒗
𝒙= -16𝒙= -16 𝒙 = 16
Proposed Solution: Sign flag
The logarithm of ZeroThe logarithm of Zero
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1616
The importance of ZerosThe importance of Zeros
Image compatibility Efficient coding Better compression ratio
Lineardomain
Logarithmicdomain
Lineardomain
𝒙 = 𝟎𝒙 = 𝟎 Ex. L = 0 * yEx. L = 0 * y
𝒗 = 𝟎𝒗 = 𝟎
𝒙 = 𝟎𝒙 = 𝟎 𝑳 = 𝟎𝑳 = 𝟎
Proposed solution: Virtual Zero
𝒍𝒐𝒈 𝟎 = −∞
Logarithmic Quantization : LNS-QLogarithmic Quantization : LNS-Q
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1717
LNS-Q Features
Novel quantization method → scaling
Controlled Precision → 𝒏𝒇 Limited quality degradation
Smaller quantization error
𝑿𝑳𝑵𝑺−𝑸 = 𝒓𝒐𝒖𝒏𝒅 𝒙 × 𝑺𝑪
𝑺𝑪 = 𝟏𝟎𝒏𝒇 = 𝟏, 𝟏𝟎, 𝟏𝟎𝟎, 𝒆𝒕𝒄 , 𝒏𝒇 ≥ 𝟎𝒙 : un-quantized logarithmic value
𝑿𝑳𝑵𝑺−𝑸 : quantized logarithmic value
𝑺𝑪 ∶ scaling factor
𝒏𝒇 ∶ number of the fractional digits
LNS-Q
Limitation
× Quality degradation
× Large quantization error
Linear-Q
𝑿𝒒 = 𝒓𝒐𝒖𝒏𝒅𝒙
𝒒𝒔𝒕𝒆𝒑
𝒒𝒔𝒕𝒆𝒑 ≥ 𝟎 : quantization step
𝒙 : un-quantized value
𝑿𝒒 : quantized value
LNS Library StructureLNS Library Structure
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1818
LNS Library
Data
LNS-Object
𝑳 = {𝒔, 𝒗}
Sign-flag
𝑳. 𝒔𝑳. 𝒔 =
𝟎, 𝒙 ≥ 𝟎𝟏, 𝒙 < 𝟎
Value
𝑳. 𝒗𝑳. 𝒗 =
𝒍𝒐𝒈( 𝒙 ), 𝒙 ≠ 𝟎𝟎, 𝒙 = 𝟎
Operators
ADD/SUB
DIV/MUL
𝒙: linear domain
LNS Arithmetic Operators : Multiplication/DivisionLNS Arithmetic Operators : Multiplication/Division
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 1919
LNSOperator
A{v,s}A{v,s} B{v,s}B{v,s}
C{v,s}C{v,s}
𝐶. 𝑣 = 0, 𝐴. 𝑣 = 0 𝑜𝑟 𝐵. 𝑣 = 0𝐴. 𝑣 + 𝐵. 𝑣, 𝑚𝑢𝑙𝐴. 𝑣 − 𝐵. 𝑣, 𝑑𝑖𝑣
𝐶. 𝑠 = 0, 𝐴. 𝑣 = 0 𝑜𝑟 𝐵. 𝑣 = 00, 𝐴. 𝑠 = 𝐵. 𝑠1, 𝐴. 𝑠 ≠ 𝐵. 𝑠
Multiplication/DivisionMultiplication/Division
LNS Arithmetic Operators : Addition/SubtractionLNS Arithmetic Operators : Addition/Subtraction
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2020
LNSOperator
A{v,s}A{v,s} B{v,s}B{v,s}
C{v,s}C{v,s}
𝐶. 𝑣 =
𝐴. 𝑣 + 𝑓_𝑎𝑑𝑑, 𝐴. 𝑠 = 𝐵. 𝑠𝐴. 𝑣 + 𝑓_𝑠𝑢𝑏, 𝐴. 𝑠 ≠ 𝐵. 𝑠𝐴. 𝑣, 𝐵. 𝑣 = 0𝐵. 𝑣, 𝐴. 𝑣 = 00, 𝐴. 𝑣 = 𝐵. 𝑣0, 𝐴. 𝑣 = 𝐵. 𝑣 = 0
𝐶. 𝑠 = 𝐴. 𝑠, 𝐴 ≥ 𝐵𝐵. 𝑠, 𝐴 < 𝐵
𝑓_𝑎𝑑𝑑 = log 1 + 2𝐵.𝑣−𝐴.𝑣 𝑤ℎ𝑒𝑟𝑒 𝐵. 𝑣 > 𝐴. 𝑣
𝑓_𝑠𝑢𝑏 = log 1 − 2𝐵.𝑣−𝐴.𝑣 𝑤ℎ𝑒𝑟𝑒 𝐵. 𝑣 > 𝐴. 𝑣
Addition/subtractionAddition/subtraction
LNS–Library Validation MethodologyLNS–Library Validation Methodology
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2121
X
Y
LNS
Operator
(+,-,,×)
Linear
Operator
(+,-,,×)
LOG
LOG
EXP
Difference
(error)
Reference golden value
Linear domain Linear domainLogarithmic domain
LNS–Library Validation : MAC Case StudyLNS–Library Validation : MAC Case Study
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2222
Multiply and Accumulate (MAC) operation
𝑀 =
𝑖=0
𝑛
𝑎 × 𝑖
𝜖 = 𝑀𝑙𝑛𝑠 −𝑀𝑙𝑖𝑛
Error between logarithmic / linear
𝑎 : constant
𝑛 : number of iterations
𝑀𝑙𝑛𝑠 : MAC output (logarithmic)
𝑀𝑙𝑖𝑛 : MAC output (linear)
LNS Library Validation : 2D LNS-DWT ImplementationLNS Library Validation : 2D LNS-DWT Implementation
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2323
DWT-based 9/7 CDF filtero JPEG2000/WAAVES
Lifting Schemeo More efficient than the convolution approacho Less memory requirements
Validation results o Absolute error:
• between linear/logarithmic around 7×10−10
1D LNS-DWT
(rows)
1D LNS-DWT
(rows)
Horizontal Transform
1D LNS-DWT
(columns)
1D LNS-DWT
(columns)
Vertical Transform
LOGLOGInput image DWT coefficients
LNS-Q ValidationLNS-Q Validation
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2424
DW
T co
effi
cie
nt
valu
e
DWT coefficient location
Small Quantization Error
SummarySummary
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2525
• MAC → 7×10−8 (1000 iterations)
• LNS-DWT → 7×10−10
• DWT+LNS-Q
Novel LNS-Library Novel LNS-Library
Image compression compatibility
Virtual zero
Sign flag
Novel logarithmic-based Quantization method (LNS-Q)
o Scaling-based
ValidationValidation
PART IVLogarithmic DWT based Compression Logarithmic DWT based Compression
29-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 26
LNS-DWT/LNS-Q Integration with WAAVESLNS-DWT/LNS-Q Integration with WAAVES
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2727
EncoderOriginal
Image LNS-QLNS
DWT
Compression Side
EncoderCompressed
Image
.CODLOG
LimitationsLimitations Limited range of compression ratio .. Why ?
LNS Vs Linear DWT Dynamic RangeLNS Vs Linear DWT Dynamic Range
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2828
LNS
Better quality
Linear
Higher compression ratio
LNS Vs Linear DWT Data DistributionLNS Vs Linear DWT Data Distribution
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 2929
Q: How to combines the advantages of the both domains into a single bit-stream ?
LNS Linear
Hybrid-DWTHybrid-DWT
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3030
LL HL
LH HH
LNS Zeros
Zeros Zeros
Zeros Linear
Linear Linear
LNS FLP
FLP FLP
Linear DWT
LL HL
LH HH
LNS DWT Masked LNS DWT
Masked Linear DWT
Merged DWT
LNS/Linear
Stage 1 Stage 2 Stage 3DescriptionDescription
DWT coefficients → 2 parts• LL sub-band → Logarithmic
• The rest sub-bands → Linear
New compression parameter:• NL : number of linear levels
• Trade-off between
o Compression ratio
o Image quality
LNS-DWT Dynamic Range Reduction FilterLNS-DWT Dynamic Range Reduction Filter
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IssueIssue
Small values in the linear domain → large negative values in the
logarithmic domain
That affects the encoding efficiency
ObjectiveObjective
To Increase the number of zeros → improve the coding efficiency
HowHow
Threshold (FTH) is used to choose which value are removed
Replace the very large negative values with zeros in LNS-DWT
Part of DWT coefficients before quantization Part of DWT coefficients before quantization
After quantization only After quantization only
After DRRAfter DRR
LNS-WAAVES based on Hybrid-DWT LNS-WAAVES based on Hybrid-DWT
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3232
NL
B
FTH
SC
• B : Logarithmic base
• NL : Number of linear DWT level
• FTH : DRR threshold
• q : Quantization step (linear)
• SC : LNS-Q scale factor
q
Input
Image
Hybrid
DWTEncoder
(HENUC)
Compressed
ImageLOG
DRRFilter
Quantization
LNS-Q
Linear-Q
Evaluation Methodology Evaluation Methodology
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3333
Experiments outlines:
Log base effect
NL effect
DRR effect
Quantization effect
Experiments outlines:
Log base effect
NL effect
DRR effect
Quantization effect
Image
Image Quality Assessment : PSNR or SSIM ?Image Quality Assessment : PSNR or SSIM ?
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3434
𝑷𝑺𝑵𝑹 𝒅𝑩 =𝟐𝟎 𝒍𝒐𝒈𝟏𝟎(𝒎𝒂𝒙𝒊𝒎𝒖𝒎𝒑𝒊𝒙𝒆𝒍 𝒗𝒂𝒍𝒖𝒆)
𝑴𝑺𝑬
𝑺𝑺𝑰𝑴 𝒇,𝒈 =𝟐𝝁𝒇𝝁𝒈 + 𝑪𝟏 + 𝟐𝝈𝒇 𝝈𝒈 + 𝑪𝟐
(𝝁𝒇𝟐𝝁𝒈
𝟐 + 𝑪𝟏)(𝝈𝒇𝟐𝝈𝒈
𝟐 + 𝑪𝟐)
SSIM measures the image quality in terms of:
• Structural
• Luminance
• Contrast
PSNR depends only on the mean square error (MSE):
𝝁𝒇 , 𝝁𝒈 Mean intensity for images f , g
𝑪𝟏 , 𝑪𝟐 Constants
𝝈𝒇 , 𝝈𝒈 Standard deviation for images f , g
Assume an original image and a reconstructed image, f and g respectively
The change-of-base formula
Results : The Log Base EffectResults : The Log Base Effect
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3535
Yields a smaller logarithmic value
Less dynamic range
Better compression ratio
Less quality
𝑙𝑜𝑔𝑏 𝑥 =𝑙𝑜𝑔𝑘(𝑥)
𝑙𝑜𝑔𝑘(𝑏)
The higher base of a logarithm (B)
Results : The impact of LNS-Q and NLResults : The impact of LNS-Q and NL
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3636
NL = 1 → Max CR = 18 NL = 2 → Max CR = 60 NL = 3 → Max CR = 140
NL: number of linear sub-bands
Results : The Influence of the QuantizationResults : The Influence of the Quantization
29-03-201729-03-2017 3737
SSIM improvement (QUALITY)
Better than JPEG2000 by:
8% to 55% → at SC = 100
5% to 44% → at SC = 10
3% to 22% → at SC = 1
Better than WAAVES by:
7% to 44% → at SC = 100
4% to 38% → at SC = 10
2% to 10% → at SC = 1
LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA
Results : The Impact of the DRRResults : The Impact of the DRR
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3838
DRR: dynamic range reduction filter
SummarySummary
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 3939
LNS-WAAVES : novel compression schemeLNS-WAAVES : novel compression scheme
Hybrid-DWTHybrid-DWT
Merging DWT coefficients in a hybrid fashion = linear + logarithmic domains
Based on Hybrid-DWT/LNS-Q1. Log base effect
• B= 2 give the best quality due to less quantization error2. NL effect
• NL = 3 gives the best compression range3. Quality Improvement:
• of 8% up to 34% compared to WAAVES
Part V2D-DWT Hardware Architecture2D-DWT Hardware Architecture
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Introduction Introduction
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The second research question & Motivation
• Compression SPEED on embedded systems
• Smart EEG requirements
• The need of a high-speed DWT for a video compression (100 fps)
Which DWT Algorithm suitable for hardware (Lifting /convolution) ?
• Lifting is more efficient:
• Less operations
• Less memory requirements (in-place computation)
• Less memory access
DWT Algorithm IssuesDWT Algorithm Issues
29-03-201729-03-2017 PhD Defense: Mohammed IBRAHEEM 4242
Data dependency 1D-DWT for each row, THEN 1D-DWT for each column
Memory access Horizontal transform
• Read the input image• Write the output coefficients
Vertical transform• Read the horizontal coefficients • Write the output vertical coefficients
Input imageInput image
Lo
wL
ow
Hig
hH
igh
LowLow HighHighLowLow HighHigh
Lifting scheme AnalysisLifting scheme Analysis
29-03-201729-03-2017 PhD Defense: Mohammed IBRAHEEM 4343
Split input data vectorEven/Odd
Stage: Predict 1P1 = current even pix + α(pix previous + pix next)
Stage: Update 1U1 = current odd pixel + β (U1 previous + U1 next)
Stage: Predict 2P2 = current P1 + γ (U1 previous + U1 next)
Stage : Update 2U2 = current U1 + δ (P2 previous + P2 next)
Stage: Scaling P2Stage: Scaling U2
The Lifting DWT algorithmIssues
• Each stage depends on the previous one
o Parallelism complexity
• For the 2D transform
o The need to wait the row to processed before
starting processing the columns.
Solution To achieve high parallelism
Efficient memory organization
Partial DWT computation of the image
o Process few rows then,
o Start process the columns
The even/odd data in the columns
can be processed independently
4-Port Memory and External memory interface 4-Port Memory and External memory interface
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 4444
phase-locked loop
Horizontal / Vertical 1D DWTHorizontal / Vertical 1D DWT
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Simple computation units Short critical path (adder +multiplier) 8 pixels in parallel (4 odd + 4 even) Parallel horizontal/ vertical transform
Proposed Unified 2D DWT ArchitectureProposed Unified 2D DWT Architecture
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FeaturesFeatures
Line BuffersLine Buffers
[Even/Odd] pixels split on-the-fly
Novel LB scheme two features
• 4-port memories → parallel operation
Data concatenation
• 4 pixels/location
• 4 odd pixels parallel
• 4 even pixels parallel
High throughput
Parallel Horizontal/Vertical transform
Novel custom memories
Scalability
Results : Resources on DE4 FPGA boardResults : Resources on DE4 FPGA board
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Altera Stratix IV GX230 resources utilization for 1080p
Results : Architecture ScalabilityResults : Architecture Scalability
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 4848
a) FPGA prototyping results including DMAs latency
b) F_Exp: Experimental frequency in MHz
c) Maximum core logic frequency in MHz
Results : Comparison with Existing WorksResults : Comparison with Existing Works
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 4949
Aziz et al.
2012
FPGA
Sameeen et al.
2012
FPGA
Hu et al.
2013
ASIC
Hsia et al.
2013
ASIC
Darji et al.
2014
ASIC
Darji et al.
2014
FPGAThis work
Cycles/pixel 1 1.55 0.5 0.75 0.5 0.5 0.125
Frame/s 53 24 n/a n/a n/a n/a 120
DWT filter 5/3 9/7 & 5/3 9/7 9/7 & 5/3 9/7 9/7 & 5/3 9/7
Sys. Freq 221.44 133.3 50 100 100 100 125
DRR Freq n/a 266 n/a n/a n/a n/a 250
Bit/pixel 8-bit 32-bit 8-bit 16-bit n/a n/a 32-bit
Add/Mul 2/0 n/a 116/188 16/0 16/10 16/10 68/54
Critical Path 2 adders n/a Mul + add 2 Mul+ 4 Add mul Mul + add Mul + add
Scalability No No Yes No No No Yes
Frame size 512×512 1920×1080 512×512 256×256 256×256 1920×1080 1920×1080
SummarySummary
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A unified 2D DWT computation architecture
• Horizontal/Vertical transform simultaneously
4-Port Line buffers
• Eliminate the inefficient reading or writing columns of an image from/to DDR
RAM
• Parallel read/write from the external RAM
• Parallel transform
• Memory size optimization by having less temporary buffers (in-place calculation)
Throughput :
• 120 fps 1080p
Scalable architecture
• Support high resolution images up to 4K
Part VIConclusion and Future workConclusion and Future work
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An LNS library as ToolAn LNS library as Tool
The virtual zero → efficient encoding
The sign flag → solved the sign ambiguity problem
Novel quantization method LNS-Q
• Scaling operation
• Limited quantization error
Library validation → small Error compared to the FLP ≈ 𝟕 × 𝟏𝟎−𝟕
Hybrid-DWT: Sub-bands → Two parts: logarithmic + linear
Advantages
Enhanced the image quality
better compression ratio
LNS-WAAVESLNS-WAAVES
ConclusionConclusion
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LNS-WAAVES has Improvement in the quality:
8% up to 34% better than WAAVES
10% up to 49% better than JPEG2000
ConclusionConclusion
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A high throughput of 8 pixels/clock cycle
Processing speed up to 120 fps Full-HD
Novel DWT ArchitectureNovel DWT Architecture
Key FeaturesKey Features
A unified 2D DWT computation
parallel Horizontal/Vertical transform
4-port line buffers → parallel process :
DMA reading/writing
Horizontal/vertical
A 2x reduction in the required DDR RAM bandwidth
Scalable architecture
Future workFuture work
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Image compression based logarithmic arithmetic is a promising research area
Exploring the WAAVES HENUC encoder• How to switch the encoding algorithm into the logarithmic domain
• Scan/sort
• To be adapted with the logarithmic DWT coefficients
Building a logarithmic computation unit and integrating it with proposed architecture
• support the hybrid-dwt
Lossless compression mode by including the DWT LeGall 5/3
Logarithmic compression Logarithmic compression
Embedded SystemsEmbedded Systems
List of Publications : 2 International Journals List of Publications : 2 International Journals
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 5555
L. Lambert, J. Despatin, I. Dhif, I. Mhedhbi, M. Shaaban Ibraheem, A. Syed-Zahid, B. Granado, K. Hachicha, A.
Pinna, P. Garda, F. Kaddouh, M. Terosiet, A. Histace, O. Romain, C. Bellet, F. Durand, J.P. Commes, S. Hochberg, D.
Heudes, P. Lozeron,and N. Kubis. “Telemedecine, electroencephalography and current issues. smart-eeg: An
innovative solution.” European Research in Telemedicine, 4(3):81 – 86, 2015.
Mohammed Shaaban Ibraheem, Khalil Hachicha, Syed Zahid Ahmed, Laurent Lambert, and Patrick Garda. “A
scalable high throughput 2d dwt architecture for a medical application”. Journal of Real-Time Image Processing,
submitted: Jan 2017. (under peer-review )
List of Publications : 4 International ConferencesList of Publications : 4 International Conferences
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 5656
Mohammed Shaaban Ibraheem, Syed Zahid Ahmed, Khalil Hachicha, Sylvain Hochberg, and Patrick Garda:
“A low ddr bandwidth 100fps 1080p video 2d discrete wavelet transform implementation on fpga”. In
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA
’16, pages 274–274, New York, NY, USA, 2016. ACM.
M. S. Ibraheem, S. Z. Ahmed, K. Hachicha, S. Hochberg, and P. Garda. “Medical images compression with
clinical diagnostic quality using logarithmic dwt.” In 2016 IEEE-EMBS International Conference on
Biomedical and Health Informatics (BHI), pages 402–405, Feb 2016.
Mohammed IBRAHEEM, Khalil Hachicha, Syed Ahmed, Sylvain Hochberg, and Patrick Garda.
“Logarithmic discrete wavelet transform for medical image compression with diagnostic quality.” In
Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare,
MOBIHEALTH’15, pages 272–275, ICST, Brussels, Belgium, Belgium, 2015. ICST (Institute for Computer
Sciences, Social- Informatics and Telecommunications Engineering).
Dhif, M. S. Ibraheem, L. Lambert, K. Hachicha, A. Pinna, S. Hochberg, I. Mhedhbi, and P. Garda. “A novel
approach using waaves coder for the eeg signal compression”. In 2016 IEEE-EMBS International Conference
on Biomedical and Health Informatics (BHI), pages 453–456, Feb 2016.
List of Publications : 5 National WorkshopsList of Publications : 5 National Workshops
29-03-201729-03-2017 LNS-DWT Mohammed IBRAHEEM – LIP6-UMPC-CIRA 5757
M. Shaaban Ibraheem, Khalil Hachicha, Imen Mhedbi, Sylvain Hochberg, Patrick Garda, S. Zahid Ahmed. “Logarithmic-
based dwt for medical images compression”. In Colloque de la Fédération d’Électronique, Thème : Internet des objets pour
les applications biomédicales, Issy-les-Moulineaux, France, 2016.
M. Shaaban Ibraheem, Sylvain Hochberg, Patrick Garda, Syed Zahid Ahmed. “Study of applying logarithmic dwt for
medical images compression”. In 11ème Colloque du GDR SoC-SiP, Nantes, France, 2016.
L. Lambert, S. Z. Ahmed B. Granado K. Hachicha A. Pinna, M. S. Ibraheem I. Dhif and P.Garda. “Smart-eeg, a new
platform for tele-expertise of electroencephalogram.” In 11ème Colloque du GDR SoC-SiP, Nantes, France, 2016.
Dhif, I. Mhedhbi, M. Shaaban Ibraheem, A. Syed-Zahid, B.Granado, K. Hachicha, A. Pinna, P. Garda, F. Kaddouh, M.
Terosiet, A. Histace, O.Romain, C. Bellet, F. Durand, J.P. Commes, S. Hochberg, D. Heudes, P. Lozeron, N.Kubis, L.
Lambert, J. Despatin. “Telemedecine, electroencephalography and current issues smart-eeg: An innovative solution.” In
8ème édition du Congrès SFT ANTEL, Centre Universitaire des Saints Pères, Paris, 2015.
L. Lambert, M. Shaaban Ibraheem, S. Zahid Ahmed, B. Granado, K. Hachicha, A. Pinna, P. Garda,, I. Dhif, “Smart-eeg :
A new platform for tele-expertise of electroencephalogram” In GDR SOC SIP, Paris, 2014.
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Thank You !