\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Autoencoder Based Image Quality Metric for Modelling Semantic Noise in
Semantic Communications}



\author[1]{Prabhath Samarathunga}%
\author[1]{Thanuj Fernando}%
\author[1]{Vishnu Gowrisetty}%
\author[1]{Thisarani Atulugama}%
\author[1]{Prof. Anil Fernando}%
\affil[1]{University of Strathclyde}%


\vspace{-1em}



  
  \date{March 14, 2025}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Semantic communication has attracted significant attention as a key
technology for emerging 6G communications. Though it has lots of
potentials specially for high volume media communications, still there
is no proper quality metric for modelling the semantic noise in semantic
communications. This paper proposes an autoencoder based image quality
metric to quantify the semantic noise. An autoencoder is initially
trained with the reference image to generate the encoder decoder model
and calculate its latent vector space. Once it is trained, a
semantically generated/received image is inserted to the same
autoencoder to create the corresponding latent vector space. Finally,
both vector spaces are used to define the Euclidean space between two
spaces to calculate the Mean Square Error between two vector spaces,
which is used to measure the effectiveness of the semantically generated
image. Results indicate that the proposed model has a correlation
coefficient of 88\% with the subjective quality assessment. Furthermore,
the proposed model is tested as a metric to evaluate the image quality
in conventional image coding. Results indicate that the proposed model
can also be used to replace conventional image quality metrics such as
PSNR,SSIM,MSSIM,UQI, VIFP, and SSC whereas these conventional metrics
completely failed in semantic noise modelling.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

