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<p class="MsoNormal">Dear Colleagues,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Please be aware of the upcoming Special Issue: “Advances on Point Cloud Technology: From coding and quality evaluation to applications” in the EURASIP Journal on Image and Video Processing:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><a href="https://www.springeropen.com/collections/apct">https://www.springeropen.com/collections/apct</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The deadline for submission is the 30<sup>th</sup> November 2023. Please find more details in the attached document and the abstract below:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><i><span style="mso-ligatures:none;mso-fareast-language:EN-AU"><a href="https://jivp-eurasipjournals.springeropen.com/" target="_blank"><span style="color:blue">EURASIP Journal
on Image and Video Processing</span></a></span></i></b><b><span style="mso-ligatures:none;mso-fareast-language:EN-AU"> is calling for submissions to our Collection on
<i>Advances on Point Cloud technology: From coding and quality evaluation to applications</i><o:p></o:p></span></b></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="mso-ligatures:none;mso-fareast-language:EN-AU">In this special issue of the EURASIP JIVP, leading researchers and practitioners in academia, industry and standard-bodies
are invited to contribute to the advancement of the state of the art on point cloud processing technology, by submitting new coding models based either on the traditional approaches or in machine learning technology, new quality models for subjective quality
estimation, new objective quality measures, and new methods and applications of point cloud processing.<o:p></o:p></span></b></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="mso-ligatures:none;mso-fareast-language:EN-AU">Point Clouds (PC) are one of the formats being considered to represent plenoptic information. They provide a flexible
representation of 3D visual information with multiple applications to virtual, mixed and augmented reality, computer graphics modelling, medical imaging, mobile robot environment modelling for use in autonomous driving and other applications. <o:p></o:p></span></b></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="mso-ligatures:none;mso-fareast-language:EN-AU">However, the widespread adoption of point cloud applications faces several challenges, amongst which the most pressing
one is the volume of data required to represent the geometry and attributes of the increasingly larger sets of points generated by point cloud capture and generation processes and devices. <o:p></o:p></span></b></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="mso-ligatures:none;mso-fareast-language:EN-AU">Solving this problem calls for advanced coding models able to reduce the data size of point clouds to volumes compatible
with efficient transmission, storage and processing. <o:p></o:p></span></b></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="mso-ligatures:none;mso-fareast-language:EN-AU">In the last few years several point cloud coding methods have been proposed based on different representation principles,
from octree decompositions to projection onto 2D images followed by waveform coding via legacy image coders. Recently a new class of methods leveraging the power of (deep) machine learning has been proposed, achieving good performance in geometry information
coding, that is being followed by integrating machine learning based attribute compression tools. Besides the prospects of achieving coding performance (geometry and attributes) exceeding the state-of-art performance the machine learning solutions also permit
direct compressed domain processing, with significant savings in the computational costs associated with decoding operations.<o:p></o:p></span></b></p>
<p class="MsoNormal">Kind Regards,<br>
Stuart<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="margin-bottom:12.0pt"><b><span style="font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-AU">Stuart Perry</span></b><span style="font-family:"Arial",sans-serif;mso-ligatures:none;mso-fareast-language:EN-AU"><br>
Professor<br>
<br>
<b>Faculty of Engineering and IT, School of Electrical and Data Engineering</b><br>
University of Technology Sydney<br>
T. +61 (2) 9514 7605<br>
PO Box 123 Broadway NSW 2007 Australia <br>
<span style="color:#0F4BEB"><a href="http://www.uts.edu.au/"><b><span style="color:#0F4BEB">uts.edu.au</span></b></a>
</span><o:p></o:p></span></p>
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