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\begin{document}
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\begin{slide}
\heading{QCLICS}
\heading{Quality Controlled Compression of Sets of Images}
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\smallskip
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\begin{center}
presented by: \\
Viresh Ratnakar \\
University of Wisconsin-Madison \\
Email: ratnakar@cs.wisc.edu
\end{center}
\begin{center}
Joint work with: \\
Miron Livny \\
University of Wisconsin-Madison \\
Email: miron@cs.wisc.edu
\end{center}
\end{slide}


\begin{slide}
\heading{Rate-Quality tradeoff in lossy image compression}
\begin{center}\
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\epsffile{RQ.eps}
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\begin{gist}
{\highlight{Image, Compression method, and Quality metric}}
\end{gist}
\end{slide}

\begin{slide}
\heading{Quality-controlled lossy image compression}
\smallskip
\begin{itemize}
\item Use a quality metric best suited for the application
\item Provide flexibility to users in specifying arbitrary rate/quality targets
\item Achieve rate/quality targets accurately
\item Optimize rate-quality tradeoff, exploiting
       image-specific and application-specific characteristics
       efficiently
\end{itemize}
\end{slide}

\begin{slide}
\heading{Quality metrics used}
\smallskip
\begin{itemize}
\item Distortion-based metrics: PSNR, perceptually weighted PSNR
\item $\mbox{PSNR } = 10 \log_{10} \frac{{255}^2}{\mbox{Distortion}}$ dB
\end{itemize}
\end{slide}

\begin{slide}
\heading{RD-OPT: Quality-controlled JPEG compression [DCC'95,'96]}
\smallskip
\begin{itemize}
\item Chooses DCT quantization tables and a Coefficient-zeroing strategy
\item Near-optimal rate-distortion tradeoff
\item Resulting rates/distortions very close to targets
\item Efficient: can achieve targets over
   the entire rate-distortion range after one run
\item Distortion can be (perceptually) weighted in the DCT domain
\end{itemize}
\end{slide}

\begin{slide}
\heading{QCLICS: Quality-Controlled Lossy Image Compression of Sets}
\smallskip
\begin{itemize}
\item Large sets of images grouped together for compression
\item In addition to quality-control of individual images, do
      quality-control for the whole set to exploit characteristics
      of the whole set
\item Aggregate rate/quality targets with individual constraints
\item JPEG compression of images
\end{itemize}
\end{slide}

\begin{slide}
\heading{Multimedia applications of QCLICS (examples)}
\smallskip
\begin{itemize}
\item Packing images on a CD-ROM
\item Moving collections of images across a network
\item Caching images in memory
\end{itemize}
\end{slide}

\begin{slide}
\heading{Packing images on a CD-ROM}
\begin{center}\
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\epsffile{cdrom.eps}
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\begin{slide}
\heading{Moving images across networks}
\begin{center}\
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\epsffile{net.eps}
\end{center}
\end{slide}

\begin{slide}
\heading{Caching images in memory}
\begin{center}\
\epsfysize=4.5in
\epsffile{cache.eps}
\end{center}
\end{slide}

\begin{slide}
\heading{Formulating the quality-control problem for image sets}
\begin{itemize}
\item Image set ${\mathcal I} = \bigl\{I_1,I_2,\ldots,I_N\bigr\}$
\item Rate $R = \sum R_i w_i^r$ \\
$\mbox{ }\;\;\;$ simple choices for $w_i^r$: averaging rate, or total size
\item Distortion $D = \sum D_i w_i^d$ \\
$\mbox{ }\;\;\;$ simple choice for $w_i^d$: averaging distortion
\item Target rate $R^*$ or distortion $D^*$
\item Minimum and Maximum constraints on $R_i$ and $D_i$
\end{itemize}
\end{slide}

\begin{slide}
\heading{Examples}
\begin{enumerate}
\item Fitting 1000 images on a CD-ROM area of 50 Megs, with no single image quality
      worse than 35 dB (PSNR).
\item Sending 10 images per second across a network with bandwidth 700 Kbits/sec,
      with no single image taking more than 0.15 seconds
\item Caching 100 large images, originally in uncompressed form on tape,
      in an available memory of 10 Megabytes, for repeated browsing
\end{enumerate}
\end{slide}

\begin{slide}
\heading{QCLICS: basic outline}
\begin{enumerate}
\item {\highlight{RD-OPT step:}} Apply RD-OPT to each image, getting a large
      number of Rate-Distortion points
      \begin{itemize} 
      \item each point satisfies the constraints
      \item the points are equally spaced along the rate axis
      \end{itemize}
\item {\highlight{Dynamic programming step:}} Use a dynamic programming approach
      to optimize total weighted rate against total weighted distortion. 
      Recover individual compression parameters for images from the total
      target rate/distortion
\item {\highlight{Compression step:}} Compress each image according to its
      compression parameters (DCT quantization tables and zeroing strategy)
\end{enumerate}
\end{slide}

\begin{slide}
\heading{QCLICS performance: test image sets}
\ptsize{10}
\begin{itemize}
\item Brain: 11 grayscale images of cat's brains
\item Cell: 31 grayscale images of a cell
\item Crystal: 14 grayscale images of various crystals
\item Venus: 17 grayscale images of Venus
\end{itemize}
\begin{enumerate}
\item {\highlight {Achieve target average rate $R^*$ \\
    $\mbox{ }\;\;\;$ each individual rate $R_i$ being within
		    $\pm \delta_r$ of $R^*$}}
\item {\highlight {Achieve target average PSNR $Q^*$ \\
    $\mbox{ }\;\;\;$ each individual PSNR $Q_i$ being within
    $\pm \delta_q$ of $Q^*$}}
\end{enumerate}
\end{slide}

\begin{slide}
\heading{Test images}
\begin{center}
\begin{tabular}[t]{cc}%
     {\highlight{Brains}} & {\highlight{Cells}} \\
    {\epsfysize=4.5in
    \epsffile{brain.photo.ps}} &
    {\epsfysize=4.5in
    \epsffile{cell.photo.ps}}
\end{tabular}%
\end{center}
\end{slide}


\begin{slide}
\heading{Test images}
\begin{center}
\begin{tabular}[t]{cc}%
     {\highlight{Crystals}} & {\highlight{Venus}} \\
    {\epsfysize=4.5in
    \epsffile{crystal.photo.ps}} &
    {\epsfysize=4.5in
    \epsffile{venus.photo.ps}}
\end{tabular}%
\end{center}
\end{slide}

\begin{slide}
\heading{Performance with target $R^*$}
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\begin{center}
\begin{tabular}{|l|c|c|c|c|c|c|}
\hline
Name & Target $R^*$ & $\delta_r$ & Pred. $R$ & Act. $R$ &
  Pred. PSNR & Act. PSNR \\
     & (bpp) & (bpp) & (bpp) & (bpp) & (dB) & (dB) \\
\hline
Brain &   1.20 & 0.30 & 1.20 & 1.21 & 29.6 & 29.6 \\
Cell &    0.75 & 0.25 & 0.75 & 0.75 & 49.8 & 49.2 \\
Crystal & 1.50 & 0.50 & 1.50 & 1.42 & 35.7 & 35.7 \\
Venus &   0.75 & 0.25 & 0.75 & 0.74 & 58.0 & 59.3 \\
\hline
\end{tabular}
\end{center}
\end{slide}


\begin{slide}
\heading{Performance with target $Q^*$}
\ptsize{10}
\begin{center}
\begin{tabular}{|l|c|c|c|c|c|c|}
\hline
Name & Target $Q^*$ & $\delta_q$ & Pred. $R$ & Act. $R$ &
  Pred. PSNR & Act. PSNR \\
     & (dB) & (dB) & (bpp) & (bpp) & (dB) & (dB) \\
\hline
Brain &   37.0 & 04.0 & 1.79 & 1.86 & 33.0 &  33.0 \\
Cell &    50.0 & 04.0 & 0.83 & 0.79 & 50.0 & 49.4 \\
Crystal & 35.0 & 10.0 & 1.08 & 1.02 & 35.0 & 35.0 \\
Venus &   55.0 & 05.0 & 0.33 & 0.38 & 55.0 &  55.5 \\
\hline
\end{tabular}
\end{center}
\end{slide}

\begin{slide}
\heading{Average running time per image in seconds}
\begin{center}
\begin{tabular}{|l|c|c|c|c|}
\hline
Name & RD-OPT & Dynamic programming & Compression & Total \\
\hline
Brain &   33.8 & 0.2 & 1.7 & 35.7 \\
Cell  &   07.6 & 0.2 & 0.7 & 08.5 \\
Crystal & 19.3 & 0.2 & 1.8 & 21.4 \\
Venus &   11.8 & 0.1 & 0.6 & 12.4 \\
\hline
\end{tabular}
\end{center}
\end{slide}

\begin{slide}
\heading{PSNR improvements as constraints are relaxed}
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\epsffile{plot.eps}
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\begin{slide}
\heading{Conclusion}
\begin{itemize}
\item QCLICS is most useful when the image set contains
      disparate images (in terms of rate-distortion tradeoffs).
      In fact, this information  may not be known apriori, and is one
      of the ``by-products'' of QCLICS.
\item RD-OPT step dominates running time: can be reduced
      significantly by narrowing the operating range. With
      some heuristics about DCT quantization table variation
      across the rate-distortion curve, RD-OPT times of
      under 5 seconds per image can be routinely obtained.
\end{itemize}
\end{slide}



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