* More fixes in the paper.

git-svn-id: file:///srv/caca.zoy.org/var/lib/svn/research@2291 92316355-f0b4-4df1-b90c-862c8a59935f
vor 16 Jahren · ef5e8797ad
--- a/2008-displacement/README
+++ b/2008-displacement/README
@@ -4,6 +4,13 @@ find ~/4chan/unsorted-4chan/http* -name '1[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-
 # Put all my 4chan images in 100 separate /tmp directories
 for x in $(seq -w 00 09); do echo $x; mkdir -p /tmp/4chan/$x; cp $(find ~/4chan/unsorted-4chan/http* -name '1[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]'$x'.???') /tmp/4chan/$x; done

 # Results for part 1
 for x in 1 2 3 4; do
    grep '^\['$x part1/dionoea.txt | awk '{ e+=$4; ef+=$7; em+=$10; n++ } END { print e/n, ef/n, em/n }' | read a1 b1 c1
    grep '^\['$x part1/4chan.txt | awk '{ e+=$4; ef+=$7; em+=$10; n++ } END { print e/n, ef/n, em/n }' | read a2 b2 c2
    echo $(((3 * $a1 + $a2) / 4)) $(((3 * $b1 + $b2) / 4)) $(((3 * $c1 + $c2) / 4))
 done

 # Condorcet voting for phase 2 results
 #  - raster + E
 #  - raster + E_min
--- a/2008-displacement/paper/paper.tex
+++ b/2008-displacement/paper/paper.tex
@@ -39,7 +39,7 @@ Conception Artistique)}

 \begin{abstract}
 In this contribution we introduce a little-known property of error diffusion
 halftoning algorithms which we call error diffusion displacement.
 halftoning algorithms which we call {\it error diffusion displacement}.
 By accounting for the inherent sub-pixel displacement caused by the error
 propagation, we correct an important flaw in most metrics used to assess the
 quality of resulting halftones. We find these metrics to usually highly
@@ -64,8 +64,8 @@ or even the design of banknotes.
 Countless methods have been published for the last 40 years that try to best
 address the problem of colour reduction. Comparing two algorithms in terms of
 speed or memory usage is often straightforward, but how exactly a halftoning
 algorithm performs in terms of quality is a far more complex issue, as it
 highly depends on the display device and the inner workings of the human eye.
 algorithm performs quality-wise is a far more complex issue, as it highly
 depends on the display device and the inner workings of the human eye.

 Though this document focuses on the particular case of bilevel halftoning,
 most of our results can be directly adapted to the more generic problem of
@@ -81,10 +81,10 @@ ordered dither matrices \cite{bayer}. However, modern techniques such as the
 void-and-cluster method \cite{void1}, \cite{void2} allow to generate screens
 yielding visually pleasing results.

 Error diffusion dithering, introduced in 1976 by Floyd and Steinberg
 \medskip Error diffusion dithering, introduced in 1976 by Floyd and Steinberg
 \cite{fstein}, tries to compensate for the thresholding error through the use
 of feedback. Typically applied in raster scan order, it uses an error diffusion
 matrix such as the following one:
 matrix such as the following one, where $x$ denotes the pixel being processed:

 \[ \frac{1}{16} \left| \begin{array}{ccc}
 - & x & 7 \\
@@ -92,12 +92,13 @@ matrix such as the following one:

 Though efforts have been made to make error diffusion parallelisable
 \cite{parfstein}, it is generally considered more computationally expensive
 than screening.
 than screening, but carefully chosen coefficients yield good visual results
 \cite{kite}.

 Model-based halftoning is the third important algorithm category. It relies
 on a model of the human visual system (HVS) and attempts to minimise an error
 value based on that model. One such algorithm is direct binary seach (DBS)
 \cite{allebach}, also referred to as least-squares model-based halftoning
 \medskip Model-based halftoning is the third important algorithm category. It
 relies on a model of the human visual system (HVS) and attempts to minimise
 an error value based on that model. One such algorithm is direct binary seach
 (DBS) \cite{allebach}, also referred to as least-squares model-based halftoning
 (LSMB) \cite{lsmb}.

 HVS models are usually low-pass filters. Nasanen \cite{nasanen}, Analoui
@@ -107,7 +108,7 @@ studies \cite{mcnamara}.

 DBS yields halftones of impressive quality. However, despite efforts to make
 it more efficient \cite{bhatt}, it suffers from its large computational
 requirements and error diffusion remains a widely used technique.
 requirements and error diffusion remains a more widely used technique.

 \section{Error diffusion displacement}

@@ -120,8 +121,8 @@ improve the image quality significantly.
 Intuitively, as the error is always propagated to the bottom-left or
 bottom-right of each pixel (Fig. \ref{fig:direction}), one may expect the
 resulting image to be slightly translated. This expectation is confirmed
 when alternatively viewing an error diffused image and the corresponding DBS
 halftone.
 visually when rapidly switching between an error diffused image and the
 corresponding DBS halftone.

 \begin{figure}
  \begin{center}
@@ -133,8 +134,8 @@ halftone.

 This small translation is visually innocuous but we found that it means a lot
 in terms of error computation. A common way to compute the error between an
 image $h_{i,j}$ and the corresponding halftone $b_{i,j}$ is to compute the
 mean square error between modified versions of the images, in the form:
 image $h_{i,j}$ and the corresponding binary halftone $b_{i,j}$ is to compute
 the mean square error between modified versions of the images, in the form:

 \begin{equation}
  E(h,b) = \frac{(||v * h_{i,j} - v * b_{i,j}||_2)^2}{wh}
@@ -143,15 +144,15 @@ mean square error between modified versions of the images, in the form:
 \noindent where $w$ and $h$ are the image dimensions, $*$ denotes the
 convolution and $v$ is a model for the human visual system.

 To compensate for the slight translation experienced in the halftone, we
 use the following error metric instead:
 To compensate for the slight translation observed in the halftone, we use the
 following error metric instead:

 \begin{equation}
  E_{dx,dy}(h,b) = \frac{(||v * h_{i,j} - v * t_{dx,dy} * b_{i,j}||_2)^2}{wh}
 \end{equation}

 \noindent where $t_{dx,dy}$ is an operator which translates the image along the
 $(dx,dy)$ vector.
 $(dx,dy)$ vector. By design, $E_{0,0} = E$.

 A simple example can be given using a Gaussian HVS model:

@@ -184,33 +185,32 @@ call the local minimum $E_{min}$:
  \end{center}
 \end{figure}

 For instance, a Floyd-Steinberg dither of \textit{Lena}, with $\sigma = 1.2$
 yields a per-pixel mean square error of $8.51\times10^{-4}$. However, when
 taking the displacement into account, the error becomes $7.77\times10^{-4}$
 for $(dx,dy) = (0.167709,0.299347)$. The new, corrected error is significantly
 smaller, with the exact same input and output images.
 For instance, a Floyd-Steinberg dither of \textit{Lena} with $\sigma = 1.2$
 yields a per-pixel mean square error of $3.67\times10^{-4}$. However, when
 taking the displacement into account, the error becomes $3.06\times10^{-4}$ for
 $(dx,dy) = (0.165,0.293)$. The new, corrected error is significantly smaller,
 with the exact same input and output images.

 Experiments show that the corrected error is always noticeably smaller except
 in the case of images that are already mostly pure black and white. The
 experiment was performed on a database of 10,000 images from common computer
 vision sets and from the image board \textit{4chan}, providing a representative
 sampling of the photographs, digital art and business graphics widely exchanged
 on the Internet.
 on the Internet nowadays \cite{4chan}.

 In addition to the classical Floyd-Steinberg and Jarvis, Judice and Ninke
 kernels, we tested two serpentine error diffusion algorithms: Ostromoukhov's
 simple error diffusion \cite{ostromoukhov}, which uses a variable coefficient
 kernel, and Wong and Allebach's optimum error diffusion kernel \cite{wong}.
 In addition to the classical Floyd-Steinberg and Jarvis-Judice-Ninke kernels,
 we tested two serpentine error diffusion algorithms: Ostromoukhov's simple
 error diffusion \cite{ostromoukhov}, which uses a variable coefficient kernel,
 and Wong and Allebach's optimum error diffusion kernel \cite{wong}.

 \begin{center}
  \begin{tabular}{|l|l|l|}
  \begin{tabular}{|l|c|c|}
  \hline
  & $E$ & $E_{min}$ \\ \hline
  raster Floyd-Steinberg & 0.00089705 & 0.000346514 \\ \hline
  raster Ja-Ju-Ni & 0.0020309 & 0.000692003 \\ \hline
  Ostromoukhov & 0.00189721 & 0.00186343 \\ \hline
 %    raster optimum kernel & 0.00442951 & 0.00135092 \\ \hline
  optimum kernel & 0.00146338 & 0.00136522 \\
  &~ $E\times10^4$ ~&~ $E_{min}\times10^4$ ~\\ \hline
  ~raster Floyd-Steinberg ~&~ 3.7902 ~&~ 3.1914 ~\\ \hline
  ~raster Ja-Ju-Ni        ~&~ 9.7013 ~&~ 6.6349 ~\\ \hline
  ~Ostromoukhov           ~&~ 4.6892 ~&~ 4.4783 ~\\ \hline
  ~optimum kernel         ~&~ 7.5209 ~&~ 6.5772 ~\\
  \hline
  \end{tabular}
 \end{center}
@@ -226,7 +226,7 @@ halftones}
 We have seen that for a given image, $E_{min}(h,b)$ is a better and fairer
 visual error measurement than $E(h,b)$. However, its major drawback is that it
 is highly computationally expensive: for each image, the new $(dx,dy)$ values
 need to be calculated to minimise the energy value.
 need to be calculated to minimise the error value.

 Fortunately, we found that for a given raster or serpentine scan
 error diffusion algorithm, there was often very little variation in
@@ -270,13 +270,13 @@ is a lot faster to compute than $E_{min}$, and as it is statistically closer to
 $E_{min}$, we can expect it to be a better error estimation than $E$.

 \begin{center}
  \begin{tabular}{|l|l|l|l|l|}
  \begin{tabular}{|l|c|c|c|c|}
  \hline
  & $E$ & $dx$ & $dy$ & $E_{fast}$ \\ \hline
  raster Floyd-Steinberg & 0.00089705 & 0.16 & 0.28 & 0.00083502 \\ \hline
  raster Ja-Ju-Ni & 0.0020309 & 0.26 & 0.76 & 0.00192991 \\ \hline
  Ostromoukhov & 0.00189721 & 0.00 & 0.19 & 0.00186839 \\ \hline
  optimum kernel & 0.00146338 & 0.00 & 0.34 & 0.00138165 \\
  &~ $E\times10^4$ ~&~ $dx$ ~&~ $dy$ ~&~ $E_{fast}\times10^4$ ~\\ \hline
  ~raster Floyd-Steinberg ~&~ 3.7902 ~&~ 0.16 ~&~ 0.28 ~&~ 3.3447 ~\\ \hline
  ~raster Ja-Ju-Ni        ~&~ 9.7013 ~&~ 0.26 ~&~ 0.76 ~&~ 7.5891 ~\\ \hline
  ~Ostromoukhov           ~&~ 4.6892 ~&~ 0.00 ~&~ 0.19 ~&~ 4.6117 ~\\ \hline
  ~optimum kernel         ~&~ 7.5209 ~&~ 0.00 ~&~ 0.34 ~&~ 6.8233 ~\\
  \hline
  \end{tabular}
 \end{center}
@@ -290,53 +290,53 @@ computationally expensive algorithms such as DBS try to minimise.
 Our first experiment was a study of the Floyd-Steinberg-like 4-block error
 diffusion kernels. According to the original authors, the coefficients were
 found "mostly by trial and error" \cite{fstein}. With our improved metric, we
 now have the tools to confirm or infirm Floyd and Steinberg's initial proposal.
 now have the tools to confirm or infirm Floyd and Steinberg's initial choice.

 We chose to do an exhaustive study of every $\frac{1}{16}\{a,b,c,d\}$ integer
 combination. We deliberately chose positive integers whose sum is 16. Error
 combination. We deliberately chose positive integers whose sum was 16: error
 diffusion coefficients smaller than zero or adding up to more than 1 are known
 to be unstable \cite{stability}, and diffusing less than 100\% of the error is
 known to cause important error in shadow and highlight areas of the image.
 to be unstable \cite{stability}, and diffusing less than 100\% of the error
 causes important loss of detail in the shadow and highlight areas of the image.

 First we studied all possible coefficients on a pool of 250 images with an
 error metric $E$ based on a standard Gaussian HVS model. Since we are studying
 algorithms on different images but error values are only meaningful for a given
 image, we chose a Condorcet voting scheme to determine winners. $E_{min}$ is
 only given here as an indication and had no role in the computation:
 We studied all possible coefficients on a pool of 3,000 images with an error
 metric $E$ based on a standard Gaussian HVS model. $E_{min}$ is only given here
 as an indication and only $E$ was used to elect the best coefficients:

 \begin{center}
  \begin{tabular}{|c|c|c|c|}
  \hline
  rank & coefficients & $E$ & $E_{min}$ \\ \hline
  1 & 8 3 5 0 & 0.00129563 & 0.000309993 \\ \hline
  2 & 7 3 6 0 & 0.00131781 & 0.000313941 \\ \hline
  3 & 9 3 4 0 & 0.00131115 & 0.000310815 \\ \hline
  4 & 9 2 5 0 & 0.00132785 & 0.000322754 \\ \hline
  5 & 8 4 4 0 & 0.0013117 & 0.00031749 \\ \hline
  \dots & \dots & \dots & \dots \\
  ~ rank ~&~ coefficients ~&~ $E$ ~&~ $E_{min}$ ~\\ \hline
  ~ 1 ~&~ 8 3 5 0 ~&~ 0.00129563 ~&~ 0.000309993 ~\\ \hline
  ~ 2 ~&~ 7 3 6 0 ~&~ 0.00131781 ~&~ 0.000313941 ~\\ \hline
  ~ 3 ~&~ 9 3 4 0 ~&~ 0.00131115 ~&~ 0.000310815 ~\\ \hline
  ~ 4 ~&~ 9 2 5 0 ~&~ 0.00132785 ~&~ 0.000322754 ~\\ \hline
  ~ 5 ~&~ 8 4 4 0 ~&~ 0.00131170 ~&~ 0.000317490 ~\\ \hline
  ~ \dots ~&~ \dots ~&~ \dots ~&~ \dots ~\\
  \hline
  \end{tabular}
 \end{center}

 The exact same operation using $E_{min}$ as the decision variable yields very
 different results. Again, $E$ is only given here as an indication:
 different results. Similarly, $E$ is only given here as an indication:

 \begin{center}
  \begin{tabular}{|c|c|c|c|}
  \hline
  rank & coefficients & $E_{min}$ & $E$ \\ \hline
  1 & 7 3 5 1 & 0.000306251 & 0.00141414 \\ \hline
  2 & 6 3 6 1 & 0.000325488 & 0.00145197 \\ \hline
  3 & 8 3 4 1 & 0.000313537 & 0.00141632 \\ \hline
  4 & 7 3 4 2 & 0.000336239 & 0.00156376 \\ \hline
  5 & 6 4 5 1 & 0.000333702 & 0.00147671 \\ \hline
  \dots & \dots & \dots & \dots \\
  ~ rank ~&~ coefficients ~&~ $E_{min}$ ~&~ $E$ ~\\ \hline
  ~ 1 ~&~ 7 3 5 1 ~&~ 0.000306251 ~&~ 0.00141414 ~\\ \hline
  ~ 2 ~&~ 6 3 6 1 ~&~ 0.000325488 ~&~ 0.00145197 ~\\ \hline
  ~ 3 ~&~ 8 3 4 1 ~&~ 0.000313537 ~&~ 0.00141632 ~\\ \hline
  ~ 4 ~&~ 7 3 4 2 ~&~ 0.000336239 ~&~ 0.00156376 ~\\ \hline
  ~ 5 ~&~ 6 4 5 1 ~&~ 0.000333702 ~&~ 0.00147671 ~\\ \hline
  ~ \dots ~&~ \dots ~&~ \dots ~&~ \dots ~\\
  \hline
  \end{tabular}
 \end{center}

 Our improved metric was able to confirm that the original Floyd-Steinberg
 coefficients were indeed the best possible for raster scan.
 Our improved metric allowed us to confirm that the original Floyd-Steinberg
 coefficients were indeed amongst the best possible for raster scan.
 More importantly, using $E$ as the decision variable may have elected
 $\frac{1}{16}\{8,4,4,0\}$, which is in fact a poor choice.

 For serpentine scan, however, our experiment suggests that
 $\frac{1}{16}\{7,4,5,0\}$ is a better choice than the Floyd-Steinberg
@@ -349,7 +349,7 @@ coefficients that have nonetheless been widely in use so far (Fig.
    \caption{halftone of \textit{Lena} using serpentine error diffusion and
             the optimum coefficients $\frac{1}{16}\{7,4,5,0\}$ that improve
             on the standard Floyd-Steinberg coefficients in terms of visual
             quality for a given HVS model}
             quality for the HVS model studied in section 3.}
    \label{fig:lena7450}
  \end{center}
 \end{figure}
@@ -364,9 +364,8 @@ we hope to see even more development in simple error diffusion methods.
 Confirming Floyd and Steinberg's 30-year old "trial-and-error" result with our
 work is only the beginning: future work may cover more complex HVS models,
 for instance by taking into account the angular dependance of the human eye
 \cite{sullivan}. And now that we have a proper metric, we plan to improve all
 error diffusion methods that may require fine-tuning of their propagation
 coefficients.
 \cite{sullivan}. We plan to use our new metric to improve all error diffusion
 methods that may require fine-tuning of their propagation coefficients.

 %
 % ---- Bibliography ----
@@ -400,7 +399,7 @@ P. Metaxas,
 Color Imaging: Device-Indep. Color, Color Hardcopy, and Graphic Arts IV, Proc.
 SPIE 3648, 485--494 (1999)

 \bibitem[6]{quality}
 \bibitem[6]{kite}
 T. D. Kite,
 \textit{Design and Quality Assessment of Forward and Inverse Error-Diffusion
 Halftoning Algorithms}.
@@ -409,38 +408,38 @@ PhD thesis, Dept. of ECE, The University of Texas at Austin, Austin, TX, Aug.

 \bibitem[7]{halftoning}
 R. Ulichney,
 \textit{Digital Halftoning}
 \textit{Digital Halftoning}.
 MIT Press, 1987

 \bibitem[8]{spacefilling}
 L. Velho and J. Gomes,
 \textit{Digital halftoning with space-filling curves}
 \textit{Digital halftoning with space-filling curves}.
 Computer Graphics (Proceedings of SIGGRAPH 91), 25(4):81--90, 1991

 \bibitem[9]{peano}
 I.~H. Witten and R.~M. Neal,
 \textit{Using peano curves for bilevel display of continuous-tone images}
 \textit{Using peano curves for bilevel display of continuous-tone images}.
 IEEE Computer Graphics \& Appl., 2:47--52, 1982

 \bibitem[10]{nasanen}
 R. Nasanen,
 \textit{Visibility of halftone dot textures}
 \textit{Visibility of halftone dot textures}.
 IEEE Trans. Syst. Man. Cyb., vol. 14, no. 6, pp. 920--924, 1984

 \bibitem[11]{allebach}
 M. Analoui and J.~P. Allebach,
 \textit{Model-based halftoning using direct binary search}
 \textit{Model-based halftoning using direct binary search}.
 Proc. of SPIE/IS\&T Symp. on Electronic Imaging Science and Tech.,
 February 1992, San Jose, CA, pp. 96--108

 \bibitem[12]{mcnamara}
 Ann McNamara,
 \textit{Visual Perception in Realistic Image Synthesis}
 \textit{Visual Perception in Realistic Image Synthesis}.
 Computer Graphics Forum, vol. 20, no. 4, pp. 211--224, 2001

 \bibitem[13]{bhatt}
 Bhatt \textit{et al.},
 \textit{Direct Binary Search with Adaptive Search and Swap}
 \textit{Direct Binary Search with Adaptive Search and Swap}.
 \url{http://www.ima.umn.edu/2004-2005/MM8.1-10.05/activities/Wu-Chai/halftone.pdf}

 \bibitem[14]{4chan}
@@ -449,35 +448,29 @@ moot,

 \bibitem[15]{wong}
 P.~W. Wong and J.~P. Allebach,
 \textit{Optimum error-diffusion kernel design}
 \textit{Optimum error-diffusion kernel design}.
 Proc. SPIE Vol. 3018, p. 236--242, 1997

 \bibitem[16]{ostromoukhov}
 Victor Ostromoukhov,
 \textit{A Simple and Efficient Error-Diffusion Algorithm}
 \textit{A Simple and Efficient Error-Diffusion Algorithm}.
 in Proceedings of SIGGRAPH 2001, in ACM Computer Graphics,  Annual Conference
 Series, pp. 567--572, 2001

 \bibitem[17]{lsmb}
 T.~N. Pappas and D.~L. Neuhoff,
 \textit{Least-squares model-baed halftoning}
 \textit{Least-squares model-based halftoning}.
 in Proc. SPIE, Human Vision, Visual Proc., and Digital Display III, San Jose,
 CA, Feb. 1992, vol. 1666, pp. 165--176

 \bibitem[18]{stability}
 R. Eschbach, Z. Fan, K.~T. Knox and G. Marcu,
 \textit{Threshold Modulation and Stability in Error Diffusion}
 \textit{Threshold Modulation and Stability in Error Diffusion}.
 in Signal Processing Magazine, IEEE, July 2003, vol. 20, issue 4, pp. 39--50

 \bibitem[19]{kite}
 T.~D. Kite,
 \textit{Design and quality assessment of forward and inverse error diffusion
 halftoning algorithms}
 Ph.~D. in Electrical Engineering (Image Processing), August 1998

 \bibitem[20]{sullivan}
 \bibitem[19]{sullivan}
 J. Sullivan, R. Miller and G. Pios,
 \textit{Image halftoning using a visual model in error diffusion}
 \textit{Image halftoning using a visual model in error diffusion}.
 J. Opt. Soc. Am. A, vol. 10, pp. 1714--1724, Aug. 1993

 \end{thebibliography}