PCS 2021 : Picture Coding Symposium

  in Conferences   Posted on September 21, 2020

Conference Information

Submission Deadline Sunday 07 Feb 2021 Proceedings indexed by :
Conference Dates Jun 29, 2021 - Jul 2, 2021
Conference Address Bristol, United Kingdom
Conference & Submission Link https://pcs2021.org/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 1.65
#Contributing Top Scientists 14
#Papers published by Top Scientists 36
Google Scholar H5-index 4
Number of Editions: 35
Guide2Research Overall Ranking: 568
Category Rankings
Image Processing & Computer Vision 71
Proceedings https://ieeexplore.ieee.org/xpl/conhome/1002806/all-proceedings

Conference Call for Papers

The Picture Coding Symposium (PCS) is hosted and organized by Bristol Vision Institute at the University of Bristol. PCS is the pioneer conference in this area and, since 1969, has provided the most engaging forum for the visual coding community attracting world-leading academics and industrialists. PCS typically attracts industrial and academic experts from across the globe. Topics for PCS2021 include (but are not limited):

Coding of still and moving pictures; Model-based and synthetic coding
Multimodal coding and processing; Compression of virtual, augmented and mixed reality
360-degree and multi-view video coding; Representation, analysis and coding of 3D scenes
Machine-learning for image/video analysis and compression. Datasets for training and testing
Emerging standards for visual data coding; Coding of screen content and desktop sharing
Perceptually optimized objective quality metrics; Subjective quality assessment
Transcoding and adaptive streaming; Error robustness, resilience and concealment
Network-, edge- and cloud-based coding; Optimized streaming
Low complexity video compression; Energy management in compression
Hardware architectures
Real-time video communication; both peer-to-peer and multi-party video conferencing
Protection and integrity of visual data

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