dynamic range independent image quality...
TRANSCRIPT
Dynamic Range Independent
Image Quality Assessment
Tunç Aydin*, Rafał Mantiuk,
Karol Myszkowski and Hans-Peter Seidel
MPI Informatik
Image Quality Assessment
Example Applications
Global Illumination
Speed up rendering
without affecting
quality
[Myszkowski 2002]
Benchmarking
systems and
algorithms
[Dabov 2008]
Image Processing Image Compression
How much
compression
without visible
artifacts?
Subjective Experiments
Quality of the
distorted
image ?
Rate
the
Quality
+ Reliable - High cost
Simple Quality Metrics
MSE ~ 280 MSE ~ 280 MSE ~ 280 !
Based on the Differences between Images
Examples: Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR)
Random Noise Blur ~15% Decreased
Luminance
Reference
Human Visual System (HVS)
Based Metrics
Based on the Visible Differences between Images Examples: Visible Differences Predictor (VDP) [Daly 93], HDR-VDP [Mantiuk et al. 05],
Visual Discrimation Model (VDM) [Lubin 95]
Probability of Detection:
~15% Decreased
Luminance
Random Noise Distortion
Map
Distortion
Map
From Distortion Magnitude to
Structural Similarity
Measure the Preservance of Image Structure
Examples: Structural Similarity Index Metric (SSIM) [Wang et al. 04]
Reference Contrast
Enhancement Distortions may be
Visible
but
Not
objectionable
Visibility Structure
Simple metrics No No
HVS based
metrics
Limited or full
dynamic range No
Structural
similarity
based metrics
Calibration
challenging due
to “abstract”
parameters
Yes
Our approach: Hybrid of HVS and
Structural Similarity
Focus Point: Image Pair with
Different Dynamic Ranges
Similar
appearance …
… yet very
different
luminance
Outline
Detecting visibility thresholds
Full Dynamic Range Human Visual System
(HVS) Model
Detecting structural changes
A set of new distortion measures
Advantages over previous work
Possible applications
Human Visual System
(HVS) Model
…of the entire visible dynamic range
Human Visual System Model
Luminance
Masking
Contrast
Sensitivity
Light
Scattering
[ JND ]
[ LUMINANCE ]
Channel
Decomposition
Luminance
Masking Contrast
Sensitivity
Light
Scattering Channel
Decomposition
Decreased sensitivity due to glare around bright spots
[Deeley et al. 1991]
Luminance
Masking Contrast
Sensitivity
Light
Scattering
Decreased sensitivity due to glare around bright spots
[Deeley et al. 1991]
Channel
Decomposition
Luminance
Masking Contrast
Sensitivity
Channel
Decomposition
Light
Scattering
Log
Luminance
# of
JNDs
Transform image luminance to Just Noticeable Difference (JND) Space
[Mantiuk et al. 2005]
Luminance
Masking Contrast
Sensitivity
Light
Scattering Channel
Decomposition
Low
Sensitivity Low
Sensitivity
Decreased Sensitivity of very low and high frequencies [Daly 1993]
Spatial
Freq.
Contrast
Luminance
Masking Contrast
Sensitivity
Channel
Decomposition
Light
Scattering
…
…
. . . . . .
6 Frequency
Bands
6 Orientations Low Pass
Image
Cortex Transform [Watson 1987, Daly 1993]
Distortion Measures
Loss of Visible Contrast
REFERENCE
Contrast
Visibility
Threshold
Loss of Visible Contrast
TEST
REFERENCE
Contrast
Visibility
Threshold
Loss of Visible Contrast
Reference Test (Clipping)
Distortion map
Amplification of Invisible Contrast
REFERENCE
Contrast
Visibility
Threshold
Amplification of Invisible Contrast
TEST
REFERENCE
Contrast
Visibility
Threshold
Amplification of Invisible Contrast
Reference Distortion map* Test (Contouring)
*For clarity, visible
contrast loss is
not shown
Reversal of Visible Contrast
Contrast
REFERENCE
Reversal of Visible Contrast
Contrast
Visibility
Threshold
Visibility
Threshold
TEST
REFERENCE
Reversal of Visible Contrast
Reference Local contrast
reversal
No Structural Distortion
Visibility
Threshold
Visibility
Threshold
Visualization
Advantages over previous
metrics
Case Study
Local Gaussian Blur
HDR Test HDR Reference LDR Test LDR Reference
(1) HDR pair
HDR-VDP Our Metric SSIM
Loss
Amplification
Reversal
Distortion
(2) LDR pair
HDR-VDP Our Metric SSIM
Loss
Amplification
Reversal
Distortion
(3) HDR test, LDR reference
HDR-VDP Our Metric SSIM
Distortion Loss
Amplification
Reversal
(4) LDR test, HDR reference
HDR-VDP Our Metric SSIM
Loss
Amplification
Reversal
Distortion
Detecting distortions
HDR-VDP
SSIM
Sharpening Blur REFERENCE
Detecting “types” of distortions
Our
Method
Sharpening Blur REFERENCE
Loss
Amplification
Reversal
Applications
TMO Evaluation
FATTAL
PATTANAIK
Loss
Amplification
Reversal
REFERENCE
Inverse TMO Evaluation
Loss
Amplification
Reversal
REFERENCE LDR2HDR
Display Comparison (1) BrightSide DR37-P HDR Display (2000 cd/m2)
REFERENCE
Loss
Amplification
Reversal
Loss
Amplification
Reversal
Display Comparison (2) Barco Coronis 3MP LCD Display (400 cd/m2)
Loss
Amplification
Reversal
Display Comparison (3) Samsung SGH-D500 Cell Phone Display (30 cd/m2)
Summary
• Hybrid approach: HVS and structure
• Comparing different dynamic ranges
• Detecting “type” of distortions
• Applications on (inverse) tone mapping
and display comparison
• TODO
– Color
– Supra-Threshold
Luminance
Masking Contrast
Sensitivity
Light
Scattering
Decreased sensitivity due to glare around bright spots
[Deeley et al. 1991]
Channel
Decomposition