View
494
Download
2
Category
Tags:
Preview:
DESCRIPTION
Presentation at the Electronic Imaging Symposium, Jan. 2012
Citation preview
Measurement of Texture Loss for JPEG 2000 Compression
Peter D. Burns and Don Williams*Burns Digital Imaging and *Image Science Associates
Presented at IS&T and SPIE Electronic Imaging Symposium, Jan. 2012© copyright 2012 Peter D. Burns
Full paper available here
IS&T and SPIE Electronic Imaging 2012 2
Introduction
MTF established as a metric for the capture and retention of image detail
Texture-loss MTF using targets with random objects• Dead-leaves target analysis based on noise-power
spectrum
We apply this method to image detail loss during image compression
Adapt method when printed test target is not used
Compare results for JPEG2000 and JPEG with Structured Similarity Index (SSIM)
IS&T and SPIE Electronic Imaging 2012 3
Dead-Leaves MTF Measurement
Aimed at providing an effective MTF for image fluctuations (signals) influenced by adaptive or signal-dependent image processing
• e.g., adaptive noise cleaning, which could leave edge untouched, but reduce detail in important ‘textured regions’
Being developed as part of the CPIQ Initiative
Based on input and output Noise-power spectrum
filterednoisy
IS&T and SPIE Electronic Imaging 2012 4
Texture MTF using Noise-power Spectrum*
Printed Test chart
Digital image
One-dimensional Noise-power spectra
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
102
103
104
Frquency, cy/mm
Pow
er S
pect
rum
Input target
JPEG 2000
Digital camera, image processing
____________________* Also called power spectral density
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Frquency, cy/mm
MT
F txt
Texture MTF
IS&T and SPIE Electronic Imaging 2012 5
Noise-power Spectrum: meaning and measurement
• Noise-Power spectrum: for a random process, the NPS describes the fluctuations as a function of spatial frequency
Technically: Fourier transform of the spatial autocovariance
• Measurement: Average square of the Discrete Fourier Transform of a nominally uniform data array
Select data array
Compute2D FFT
Compute modulus squared
Basic steps for NPS estimation
1 or 2D
Vari
ance
/fre
quency
frequencyo
FineCoarse
IS&T and SPIE Electronic Imaging 2012 6
Recipe:
Transform the captured image data to luminance
Compute the power-spectral density as the square of the amplitude of the two-dimensional DFT of the array
Divide this array, frequency-by-frequency, by the spectrum for the input target
Compute the square-root, frequency-by-frequency
Radial-average of this array is the one-dimensional MTF vector
Compute (visually -weighted) acutance measure
Proposed Dead-Leaves MTF Measurement
IS&T and SPIE Electronic Imaging 2012 7
Proposed method for camera evaluation (basic steps)
Printed targetDigital image
Transform to luminance
signal spectrum
1. Compute corrected signal spectrum
2. Texture MTF
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Frquency, cy/mm
MT
F txt
Model target signal spectrum, Starget
3. Texture acutance metric
Acutancemetric
texture MTF
IS&T and SPIE Electronic Imaging 2012 8
Modified method (details)Input image
Output imageTransform to
luminance
2. Texture MTF
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Frquency, cy/mm
MT
F txt
Measured input signal spectrum, Starget
targetsignal SSR /'
vR
vMTFrad
3. Texture acutance metric
vCSFvMvMTFv
vrad
max
1
Acutancemetric
Visually-weighted summation*___________* Display MTF and viewing distance
Corrected signal spectrum
1. Compute corrected signal spectrum
2D FFT 2D FFT
Noisespectrum
noisesignal SS
Signalspectrum
Radial integration0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
102
103
104
Frquency, cy/mm
Pow
er S
pect
rum
Input target
JPEG 2000
IS&T and SPIE Electronic Imaging 2012 9
Application to Image Compression
Detectornoise
CFAsubsampling3 1 31
CFAinterpolation
Optical MTF
Input idealimage
Simulated captured image
Detectornoise
CFAsubsampling33 11 3311
CFAinterpolation
CFAinterpolation
Optical MTFOptical MTF
Input idealimage
Simulated captured image
ImageCompression
Image capture simulation
IS&T and SPIE Electronic Imaging 2012 10
JPEG 2000 and JPEG compression
JPEG2000: kdu_compress, from Kakadu Software
JPEG: as implemented in Matlab
Default settings for 24-bit color images
Compression ratios: up to 140:1
input 40:1 100:1
Example:
IS&T and SPIE Electronic Imaging 2012 11
Example texture MTF
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
102
103
104
Frquency, cy/mm
Pow
er S
pect
rum
Input target
JPEG 2000
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Frquency, cy/mm
MT
F txt
Results for 100:1 compression, and the corresponding texture MTF.
acutance = 0.82
IS&T and SPIE Electronic Imaging 2012 12
Comparison with Structured Similarity Index, SSIM
JPEG 2000 JPEG
Compression rate
Bits/pixel/ color
Texture acutance
SSI index Texture acutance
SSIM index
30 0.80 0.986 0.939 1.02 0.939
40 0.60 0.950 0.924 0.991 0.922
50 0.48 0.951 0.920 0.961 0.904
60 0.40 0.859 0.908 0.930 0.886
80 0.30 0.826 0.884 0.890 0.844
100 0.24 0.819 0.865 0.841 0.790
120 0.2 0.796 0.835 0.777 0.742
140 0.17 0.731 0.799 0.667 0.667
0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 10.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
SIMM value
Tex
ture
acu
tanc
e
JPEG 2000
JPEG• objective measure of image quality• based on image differences • visual-difference map, based on a
model of visually information• average value of the difference image
is reported as the SSIM. Wang, Z., Bovik, A.., Sheikh, H., and Simonelli, E., IEEE Trans. Image Processing, (2004)
IS&T and SPIE Electronic Imaging 2012 13
Summary
Many practical objective image quality measurements can be considered as estimates, with bias error and variation
The proposed texture MTF analysis relies on noise-power spectrum estimation
We investigated texture-loss due to JPEG 2000 and JPEG compression
Modified method was developed;• Direct input signal spectrum measurement (estimation)• Not dependent on known printed target spectrum
Results indicated stable texture MTF and acutance without date smoothing or fitting
Compared well with Structured Similarity Index, SSIM• an offset between the JPEG and JPEG 2000 images sets
pdburns@ieee.org
IS&T and SPIE Electronic Imaging 2012 14
Appendix: Example MTF based on Edge SFR and texture NPS
Edge SFR
Comparison with Texture MTF: Results for 100:1 compression ratio
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Frquency, cy/mm
MT
F txt
- - - Edge
___ Texture
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Spatial frequency, cy/pixel
SF
R
input
output
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.2
0.4
0.6
0.8
1
Spatial frequency, cy/pixel
MT
F
Recommended