[visionlist] EURASIP Journal on Image and Video Processing Special Issue : “Advances on Point Cloud Technology: From coding and quality evaluation to applications”

Tue May 30 23:43:02 -04 2023

Dear Colleagues,

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:

https://www.springeropen.com/collections/apct

The deadline for submission is the 30th November 2023. Please find more details in the attached document and the abstract below:

EURASIP Journal on Image and Video Processing<https://jivp-eurasipjournals.springeropen.com/> is calling for submissions to our Collection on Advances on Point Cloud technology: From coding and quality evaluation to applications
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.
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.
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.
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.
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.
Kind Regards,
Stuart

Stuart Perry
Professor

Faculty of Engineering and IT, School of Electrical and Data Engineering
University of Technology Sydney
T. +61 (2) 9514 7605
PO Box 123 Broadway NSW 2007 Australia
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