GPU based techniques for deep image merging

Archer, J, Leach, G and van Schyndel, R 2018, 'GPU based techniques for deep image merging', Computational Visual Media, vol. 4, no. 3, pp. 277-285.


Document type: Journal Article
Collection: Journal Articles

Title GPU based techniques for deep image merging
Author(s) Archer, J
Leach, G
van Schyndel, R
Year 2018
Journal name Computational Visual Media
Volume number 4
Issue number 3
Start page 277
End page 285
Total pages 9
Publisher Qinghua Daxue Chubanshe Youxian Gongsi
Abstract Deep images store multiple fragments perpixel, each of which includes colour and depth, unlike traditional 2D flat images which store only a single colour value and possibly a depth value. Recently, deep images have found use in an increasing number of applications, including ones using transparency and compositing. A step in compositing deep images requires merging per-pixel fragment lists in depth order; little work has so far been presented on fast approaches. This paper explores GPU based merging of deep images using different memory layouts for fragment lists: linked lists, linearised arrays, and interleaved arrays. We also report performance improvements using techniques which leverage GPU memory hierarchy by processing blocks of fragment data using fast registers, following similar techniques used to improve performance of transparency rendering. We report results for compositing from two deep images or saving the resulting deep image before compositing, as well as for an iterated pairwise merge of multiple deep images. Our results show a 2 to 6 fold improvement by combining efficient memory layout with fast register based merging.
Subject Computer Graphics
Keyword(s) composite
deep image
GPU
performance
DOI - identifier 10.1007/s41095-018-0118-8
Copyright notice © The Author(s) 2018. This article is published with open access at Springerlink.com
ISSN 2096-0433
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 6 Abstract Views  -  Detailed Statistics
Created: Tue, 26 Mar 2019, 09:36:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us