PROBLEMS AND ISSUES OF PROCESSING AND ORGANIZATION OF GRAPHICAL DATA IN VOLUME VISUALIZATION ON DISTRIBUTED SYSTEMS
Abstract
Most of current tasks in fields of science and technology are connected to visualization, which brings explicitness and informativity in management and design of natural and technical objects and processes, it also solves a number of important tasks of human-machine interface. The key problem of visualization in most cases is to display all the necessary information in allotted amount of time and system resources. In volume visualization, large amount of data is processed, which may require an excessive amount of computing resources, or it can introduce a number of problems in management of visualization process. Currently, research and study of approaches to organization of data being processed during volume visualization is crucial. Any decrease in computational costs during storing and processing the graphic data not only allows to reduce corresponding costs, but also allows to implement a number of modifications of sampling and compositing procedures, which introduces a number of possibilities for additional optimization of rendering process. This paper discusses key tasks of organizing, storing and processing graphical data in volume visualization paradigm in terms of distributed implementation. A general optimization approach suitable for distributed implementation is considered. A multiparametric optimization task is described, as well as objective function. Next, a problem of organization of graphical data based on spatial location and properties of volumetric phenomenon is reviewed. Key nuances are discussed, a solution for distributed case based on a combination of well-known approaches is described, theoretical and practical aspects are considered. An approach to a low-level implementation is considered, a technique of structuring the initial data as a marked graph by a set of properties, as well as a process of carrying out the rendering procedure on such structure is described.
References
– Ростов-на-Дону: ЮНЦ РАН, 2011. – 196 с.
2. Витиска Н.И., Гуляев Н.А. Исследование вариантов модификации модели объёмной визуа-
лизации методом трассировки лучей // Информатизация и связь. – 2016. – № 3. – C. 28-33.
3. Wang Z., Bovik A. C. Mean squared error: Love it or leave it? A new look at signal fidelity
measures // IEEE signal processing magazine. – 2009. – Vol. 26, No. 1. – P. 98-117.
4. Wang Z. et al. Image quality assessment: From error measurement to structural similarity //
IEEE Trans. Image Processing. – 2004. – Vol. 13. – P. 600-612.
5. Zhang L. et al. FSIM: A feature similarity index for image quality assessment // IEEE transactions
on Image Processing. – 2011. – Vol. 20, No. 8. – P. 2378-2386.
6. Дубовик А.А. Способ ускорения работы модуля описания моделей // Матер. 4-й Всерос-
сийской научно-технической конференции «Суперкомпьютерные технологии». – 2016.
– Т. 2. – C. 27- 30.
7. Wimmer M., Wonka P. Rendering time estimation for real-time rendering // Rendering Techniques.
– 2003. – P. 118-129.
8. Knoll A. A Survey of Octree Volume Rendering Methods // 1st IRTG Workshop. – 2006.
– P. 8-19.
9. Laine S., Karras T. Efficien2t Sparse Voxel Octrees // Transactions on Visualization & Computer
Graphics. – 2011. – Vol. 17, No. 8. – P. 1048-1059.
10. Eisemann M. et al. Fast ray/axis-aligned bounding box overlap tests using ray slopes // Journal
of graphics tools. – 2007. – Vol. 12, No. 4. – P. 35-46.
11. Витиска Н.И., Гуляев Н.А. Метод визуализации трёхмерных сцен и объектов воксельной
графики для систем имитационного моделирования // Известия ЮФУ. Технические нау-
ки. – 2015. – № 4 (165). – C. 77-87.
12. Гуляев Н.А. О подходе к исследованию зависимости точности объёмной визуализации
от свойств графических данных // Информатика, управление и системный анализ
(ИУСА-2019): Тр. V Всероссийской научной конференции молодых ученых с междуна-
родным участием. – 2018. – C. 275-284.
13. Vitiska N., Selyankin V., Gulyaev N. An Approach to Optimization of Ray-Tracing in Volume
Visualization Based on Properties of Volume Elements // Proceedings of the Third International
Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18).
IITI'18 2018. – 2019. – Vol. 874. – P. 148-158.
14. Гаврилов Н.И., Турлапов В.Е. Качественная объёмная визуализация гигавоксельных мас-
сивов в блочном представлении на примерах данных из медицины // Матер. конферен-
ции «ГрафиКон». – 2012. – C. 174-179.
15. Данилов И.Г., Гуляев Н.А., Селянкин В.В. О применимости модели графовых вычислений
при прямой объемной визуализации // Информационные технологии, системный анализ
и управление (ИТСАУ-2018): Сб. трудов XVI Всероссийской научной конференции мо-
лодых ученых, аспирантов и студентов. – 2018. – Т. 3. – С. 156-159.
16. Angles R. The Property Graph Database Model. // AMW. – 2018.
17. Barrasa J. RDF Triple Stores vs. Labeled Property Graphs: What’s the Difference? // Neo4j
Blog URL – https://neo4j.com/blog/rdf-triple-store-vs-labeled-property-graph-difference/ (да-
та обращения: 22.06.2019).
18. Heidari S. et al. Scalable graph processing frameworks: A taxonomy and open challenges //
ACM Computing Surveys (CSUR). – 2018. – Vol. 51, No. 3. – P. 60.
19. Malewicz G. et al. Pregel: a system for large-scale graph processing // Proc. of the 2010 ACM
SIGMOD International Conference on Management of data. – 2010. – P. 135-146.
20. Valiant L.G. A bridging model for parallel computation // Communications of the ACM.
– 1990. – Vol. 33, No. 8. – P. 103-111.
21. Ericson C. Real-time collision detection. – CRC Press, 2004.
