# Changeset 2289 for research/2008-displacementTweet

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Timestamp:
Apr 16, 2008, 12:11:41 AM (14 years ago)
Message:
• Applied changes suggested by reviewer #1: -Page 3: The Latex "\noindent" could be added after equations (1) and (2). -Page 3, paragraph 3: gaussian -> Gaussian -Page 3 (two times): Experiment shows -> Experiments show ??
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 r2277 HVS models are usually low-pass filters. Nasanen \cite{nasanen}, Analoui and Allebach \cite{allebach} found that using gaussian models gave visually and Allebach \cite{allebach} found that using Gaussian models gave visually pleasing results, an observation confirmed by independent visual perception studies \cite{mcnamara}. where $w$ and $h$ are the image dimensions, $*$ denotes the convolution and $v$ is a model for the human visual system. \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 where $t_{dx,dy}$ is an operator which translates the image along the $(dx,dy)$ vector. A simple example can be given using a gaussian HVS model: \noindent where $t_{dx,dy}$ is an operator which translates the image along the $(dx,dy)$ vector. A simple example can be given using a Gaussian HVS model: Experiment shows that for a given image and a given corresponding halftone, Experiments show that for a given image and a given corresponding halftone, $E_{dx,dy}$ has a local minimum almost always away from $(dx,dy) = (0,0)$ (Fig. \ref{fig:lena-min}). Let $E$ be an error metric where this remains true. We \input{lena-min} \caption{Mean square error for the \textit{Lena} image. $v$ is a simple $11\times11$ gaussian convolution kernel with $\sigma = 1.2$ and $11\times11$ Gaussian convolution kernel with $\sigma = 1.2$ and $(dx,dy)$ vary in $[-1,1]\times[-1,1]$.} \label{fig:lena-min} smaller, with the exact same input and output images. Experiment shows that the corrected error is always noticeably smaller except 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 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 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