interpolated halftoning, rehalftoning, and halftone compression prof. brian l. evans...
TRANSCRIPT
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INTERPOLATED HALFTONING, REHALFTONING, AND
HALFTONE COMPRESSION
Prof. Brian L. [email protected]
http://www.ece.utexas.edu/~bevans
Collaboration with Dr. Thomas D. Kite and Mr. Niranjan Damera-Venkata
Laboratory for Image and Video EngineeringThe University of Texas at Austin
http://anchovy.ece.utexas.edu/
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OUTLINE
Introduction to halftoning
Halftoning by error diffusion Linear gain model Modified error diffusion
Interpolated halftoning
Rehalftoning
JBIG2 halftone compression
Conclusions
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INTRODUCTION: HALFTONING
Was analog, now digital processing
Wordlength reduction for images 8-bit to 1-bit for grayscale 24-bit RGB to 8-bit for color displays 24-bit RGB to CMYK for color
printers
Applications Printers Digital copiers Liquid crystal displays Video cards
Halftoning methods Screening Error diffusion Direct binary search Hybrid schemes
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EXAMPLE HALFTONES
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Original image
Clustered dot screen
Dispersed dot screen
Direct binary search
Floyd Steinberg
Modified Diffusion
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FOURIER TRANSFORMS
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Original image
Clustered dot screen
Dispersed dot screen
Direct binary search
Floyd Steinberg
Modified Diffusion
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ERROR DIFFUSION
2-D delta-sigma modulator Noise shaping feedback coder
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Title:fs_weights.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.Title:
raster_order.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
Error filter
Raster scan order
P = PastF = Future
Serpentine scan also used
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ERROR DIFFUSION (cont.)
Quantizer
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Title:eq2.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
Title:quant_kn_approx.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.Title:
quant_ks_approx.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
Governing equations
Non-linearity difficult to analyze Linearize quantizer [Kite, Evans, Bovik & Sculley 1997]
Separate signal and noise paths [Ardalan & Paulos 1987]
Noise Path
Signal Path
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LINEAR GAIN MODEL
Quantization error correlated with input [Knox 1992]
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Floyd-Steinberg Jarvis, Judice & Ninke
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Least squares fit of quantizer input to output defines signal gain
Signal gain: Noise gain:
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GAIN MODEL PREDICTIONS
Noise transfer function (NTF)
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Predicted Measured
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Floyd-Steinberg Jarvis et al.
Signal transfer function (STF)
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Predicting Signal Gain Ks
Predict Ks from error filter as:
where,
2.017.1 RK s
didjjiHjiX
didjjiX
R22
2
),(),(
),(
X(i,j) = Fourier Transform of input to error filter
H(i,j) = Fourier Transform of error filter
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MODIFIED ERROR DIFFUSION
Efficient method of adjusting sharpness [Eschbach & Knox 1991]
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Title:nsfc_sharp_equiv.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
Title:eq12.dviCreator:dvipsk 5.58f Copyright 1986, 1994 Radical Eye SoftwarePreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
Equivalent circuit: pre-filter
L can be chosen to compensate for frequency distortion
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UNSHARPENED HALFTONES
If then (flat)
Accounts for frequency distortion
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Original image Jarvis halftone
Unsharpened halftone Residual
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INTERPOLATION
Image resizing
Different methods (increasing cost) Nearest neighbor (NN) Bilinear (BL)
Nearest neighbor, bilinear methods Low computational cost Artifacts masked by quantization
noise in halftone Correct blurring by using modified
error diffusion Halftoning
Halftone
Interpolation
Original
F(z)I(z)
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INTERPOLATION
Design L for flat transfer function using linear gain model (L is constant for a given interpolator)
Compute transfer function of interpolation by M
Compute signal tranfer function
Compute L to flatten the end-to-end transfer function of the system
)()1(1
)))(1(1()(
z
zz
HK
HLKF
s
s
y
Mx
x
Mx
NN z
z
z
zI
1
1
1
1)(z
22
1
1
1
1)(
y
Mx
x
Mx
BL z
z
z
zI z
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INTERPOLATION RESULTS
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Nearest neighbor 2 Bilinear 2
Transfer functionL = –0.0105
Transfer functionL = 0.340
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REHALFTONING
Halftone conversion, manipulation
Assume input and output are error diffused halftones Blurring corrected by using modified
error diffusion Noise leakage masked by halftoning 64 operations per pixel
For a 512 x 512 image 16 RISC MIPS 0.4 s on a 167 MHz Ultra-2
workstation
Original
Halftoning Inversehalftoning Re-halftoning
Halftone
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REHALFTONING (cont.)
Halftone conversion, manipulation
Error diffused halftones
Fixed lowpass inverse halftoning filter, compromise cut-off frequency Noise leakage masked by halftoning Correct blur by modified error diffusion Computationally efficient
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REHALFTONING (cont.)
Use linear gain model to design L for flat response
Use approximation for digital frequency:
Inverse halftoning filter is a simple separable FIR filter
L is computed to flatten the end to end transfer function of the system
We need to know halftoning filter coefficients for this scheme
Improve halftoning results using knowledge of type of halftone being rehalftoned
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REHALFTONING RESULTS
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Original image Rehalftone
Signal transfer function
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THE JBIG2 STANDARD
Lossy/lossless coding of bi-level text and halftone data
Document
Symbolregion
decoder
Halftone region
decoder
Genericrefinement
Symbol
dictionary decoding
Halftonedictionarydecoding
Memory
Memory
Scan vs. random mode
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THE JBIG2 STANDARD (cont.)
Bi-level text coding Hard pattern matching (lossy) Soft pattern matching (lossless or
near lossless) may be context based
Halftone coding Direct halftone compression Context based halftone coding Inverse halftoning and compression
of grayscale image
Implications Printers, fax machines and
scanners, will need to decode JBIG2 bitstreams
Fast decoding may require dedicated hardware and embedded software
Need for low complexity, low memory solutions
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PROBLEMS TO BE SOLVED
JBIG2 compression of halftones Compress halftone directly, using a
dictionary of patterns, or Convert halftone to grayscale
(inverse halftoning) and compress grayscale image
Efficient coding of halftone data Fax machines Digital archiving, scanning, and
copying
Fast algorithms for JBIG2 codec Interpolated halftoning in decoder
Rehalftoning in codec
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PROBLEMS TO BE SOLVED
JBIG2 embedded decoders Low memory requirements Low computational complexity High parallelism
Inverse halftoning: a robust solution for lossy coding of halftones Rendering device can use a different
halftoning scheme than encoder Multiresolution halftone rendering
(archive browsing) High halftone compression ratios
(6:1) Quality enhancement if the encoder
halftoning method is transmitted
Low-cost embedded implementations
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CONCLUSIONS
Linear gain model of error diffusion Validate accuracy of quantizer
model Link between filter gain and signal
gain
Rehalftoning and interpolation Efficient algorithms Impact on emerging JBIG2 standard
Web site for software and papers http://www.ece.utexas.edu/~bevans/
projects/inverseHalftoning.html