Several standard methods for evaluating the capture and retention of image texture are currently being developed. We review the requirements for improved texture measurement. The challenge is to evaluate image signal (texture) content without having a test signal interfere with the processing of the natural scene. We describe an approach to texture reproduction analysis that uses embedded periodic test signals within image texture regions. We describe a target that uses natural image texture combined with a multi-frequency periodic signal. This low-amplitude signal region is embedded in the texture image. Two approaches for embedding periodic test signals in image texture are described.

1. Embedded Signal Approach to Image
Texture Reproduction Analysis
Peter D. Burnsa and Donald Baxterb
aBurns Digital Imaging
bSTMicroelectronics Ltd.
Full paper:
P. D. Burns and D. Baxter, Embedded Signal Approach to Image Texture Reproduction
Analysis, Proc. SPIE Vol. 9016, Image Quality and System Performance XI, 2014

2. Introduction
1. Evolution of the intelligence and performance of camera noise-reduction
(NR) algorithms poses a significant challenge for texture
evaluation
• Algorithms are ‘content aware’ and adapt to local image content
• Deadleaves and sine-wave methods can yield results that are
inconsistent with visual impression
2. Successful methods for measuring the capture of image texture,
• Test target should have texture fluctuations similar to natural
materials such as grass, to ensure realistic NR algorithm
behavior.
• Analysis should result in a reliable texture MTF in the presence
of image noise such as that observed with current equipment
and high ISO (low-light) settings.
2

3. Introduction
• ISO 25,600 grass and Siemens Star images, processed with the same non-local
means filter strength
• grass is blurred but the low contrast Siemens Star < 0.4 cycles/pixel are not
• Siemens Star image for the non-local means filter had a constant contrast
(9% amplitude) up to 0.4cycles/pixel
3

4. Embedded test signals
We investigate the use of two types of embedded test
signals
1. Grid multi-burst method
• Low amplitude version of that used in IEC-61146-1, Video cameras -
Methods of measurement - Part 1: Non-broadcast single-sensor
cameras
2. Stacked sine-wave method
• Several frequencies combined, or stacked
4
Combine
(embed)
texture
Camera
simulation
Lens MTF
noise
Embedded signal
Image
processing
Texture MTF
Analysis
noise
reduction
signal detection
estimation
Stacked sine-wave
Multi-burst grid

5. Stacked sine-wave method
• One dimensional sine-wave signals
• Several frequencies combined, or stacked
4
0 50 100 150 200 250 300
1.8
1.6
1.4
1.2
1
0.8
0.6
x 10
pixels
signal, 16-bit values
s  a 
 f j   j  M
j n n sin 2 , 1,...,
50 100 150 200 250 300
50
100
150
200
250
300
(higher contrast than used)
N
n

1
    N  ,..., 1 = random phase values
Six-frequency stacked sine-wave region and a cross-section
5

6. Stacked sine-wave analysis method
• Discrete Fourier transform
       
U p , q s x ,
y e 
 
 
 

/ 2
/ 2 1
2
/ 2
/ 2 1
M
x M
i px qy
M
y M
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
110
100
90
80
70
60
50
40
30
20
10
0
Resolution [cycles/pixel]
Modulation [%]
• Zero-frequency cross-section
  0, p U
• Peak detection
• Example for 5 components
6

7. Embedded signal
Two types of texture natural
• Computed deadleaves
• Natural grass
Stacked sine-waves with deadleaves texture
7
Multi-burst grid with grass texture

8. Camera simulation
8
Reference Texture
Image (Linear)
Dead Leaves, Grass
Synthetic
Embedded Signal
2048x2048
linear sRGB
Synthetic Siemens
Star
Gaussian Blur
sigma = 3 pixels
Down Sample By 4
Image Sensor Noise
Photon shot and readout noise
Non Local
Means Filter
Buades
sRGB Gamma
3480 x 3240
14-bit linear sRGB
870x810
14-bit linear sRGB
Texture MTF Metrics
DFT and peak detection
2-D Sine Wave Fit
Siemens Star
870x810
8-bit sRGB
Low Pass Filter
Cut-off in cycles/pixel
Filter Order
Additive noise model
• 100 – 25600 ISO
• 2 e- readout noise
• 3.9 um pixel
• APS-C sensor
Gaussian blur after down sampling

9. Stability of the measurement without texture
9
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
100
90
80
70
60
50
40
30
20
10
0
Resolution [cycles/pixel]
Modulation [%]
ISO100
ISO400
ISO1600
ISO6400
ISO25600
Stacked Sine-wave: without texture,
for ISO 25600
Results for 6% amplitude sine-wave
and various ISO (noise) settings

10. Stability of the measurement without texture
10
Results for 6% amplitude multi-burst
and various ISO (noise) settings
Stacked sine-wave and multi-burst grid methods showed stable results
for the range of noise-levels tested.

11. Stability of the measurement with texture
11
Stacked sine-wave with computed
(dead-leaves) texture
Multi-burst with natural (grass) texture
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
100
90
80
70
60
50
40
30
20
10
0
Resolution [cycles/pixel]
Modulation [%]
ISO100
ISO400
ISO1600
ISO6400
ISO25600

12. Application to noise reduction
• Non-local means filtering: searches for a pattern around a pixel
(similarity window within a larger area)
• Adapts to local structure
• Example for grass texture alone
– Reduces higher frequency noise
– Content >0.025 cy/pixel is filtered
• With embedded sine-wave signals
– Stacked sine-waves results were visually closer than the grid
multi-burst to a visual match to the grass texture (only) after NL
filtering
12
Buades, A., Coll, B., and Morel, J., “A non-local algorithm for image denoising,” Computer Vision and Pattern
Recognition, 2, 60-65 (2005)

13. Application to noise reduction
13
ISO 25,600 after
NL filter
Grass texture
with embedded
sine-waves, after
NL filter
Multi-burst grid
Input grass
texture

14. Application to noise reduction
Without texture
• similar results except at high noise, low frequencies
14
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
110
100
90
80
70
60
50
40
30
20
10
0
Resolution [cycles/pixel]
Modulation [%]
Reference
ISO100
ISO400
ISO1600
ISO6400
ISO25600

15. Application to noise reduction
With texture
• Higher response for multi-burst method indicating contrast has been increased by
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
15
100
90
80
70
60
50
40
30
20
10
0
Resolution [cycles/pixel]
Modulation [%]
Reference
ISO100
ISO400
ISO1600
ISO6400
ISO25600
addition of grass texture
• Stacked sine-wave results were better visual match to reference (no-texture)
results

16. Conclusions
• Two embedded-signal methods for texture measurement
– Combining, or stacking several components with different frequencies
– Test by embedding on both natural and computed texture
– Low contrast so as no minimize interaction for image processing
• Camera simulation (MTF and noise) were used to generate test images
• Both embedded methods yielded stable texture-reproduction measures
for the range of exposure levels
• Stacked method can be used with a lower contrast than the grid method,
closer to visual detection
• Appearance with and without texture is similar
• Non-local mean filtering was used to test embedded methods
• Embedded methods were robust and extracted the reference (low-noise)
MTF profile
• Blur generated by NL filter for embedded stacked texture method
appeared very similar to processed regions of normal texture – a positive
result
16

17. Conclusions
• Stacked method can be used with a lower contrast than the grid
method, closer to visual detection
• Appearance with and without texture is similar
• Non-local mean filtering was used to test embedded methods
• Embedded methods were robust and extracted the reference (low-noise)
MTF profile
• Blur generated by NL filter for embedded stacked texture method
appeared very similar to processed regions of normal texture – a
positive result
17