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Methods for user guided compression algorithms Jostein Bratlie R&D Group for Mathematical Modelling, Numerical Simulation & Computer Visualization, Faculty of Technology, Narvik University College, Narvik, Norway Abstract In computer games of today and specially massively multiplayer online role playing games virtual world and the characters therein are becoming more and more realistic, and therefore demands a larger and larger amount of data to be described in detail. As a part of the ’DreamWorld’ research project, Funcom and Narvik University College have entered into a co-operation to ﬁnd new ways to handle some of the issues linked to this. This paper gives and introduction to a PhD project which aim is to develop new methods for user guided compression algorithms. It also aims to end up with a prototype for a user guided animationcompression-tool for seamless integration into an already existing production pipeline. 1 Introduction Virtual worlds are expected to be more and more realistic. With increased artistic freedom and demand for realism the amount of data describing these worlds increase exponentially for each released title. Especially with respect to MMORPGs (massively multiplayer online role playing games). As a title is released, the expectations for the level of realism in the next title will be heightened. With increased realism the ﬁrst thing that comes to mind is visual eﬀects, increased resolution of models and more stunning scenario. In addition to the sheer volume of visual eﬀects are the quality of story-telling, dialogs, voice work and acting also increasing in the same way. This again demands a richer animation base for ingame, non cinematic characters. All these diﬀerent parts yields larger amount of data, which has to be stored, processed and distributed. Todays games cinematics is also being simulated with in-game techniques, which again adds to the size of animation data. In many cases the combined size of in-game content for a computer game, again specially for an MMORPG, can reach a respectable size of up to 50100GB. Distributing content of this size by electronic means as digital downloads may be a bottleneck, demanding download times that scare oﬀ potential players. In addition there is the ever increasing amount of client/server communication, like This paper was presented at the NIK-2011 conference; see http://www.nik.no/. more complex movement which generates more positions and directional data that has to be synchronized as realism increases. So, there is a certain demand for production tools and software algorithms that give good compression, while still being easy to use and integrate into existing tool chains so that the artists will still be in control of the output. As a part of the ’DreamWorld’ research project (thanks to NFR (Norges Forskningsråd) and its ’VERDIKT’ program), Funcom and Narvik University College have entered into a co-operation which give me the possibility to work with some of these issues. The main focus for my dissertation will be on user guided compression of animation data. Possible applications is therefore user-managed methods for compression of diﬀerent types of massive data streams. This includes, but is not limited to, signal and image data as well as geometric data and data of diﬀerent structures. 2 Transforms and indexing For these kinds of tools to be interactive it is necessary to optimize this for highly parallel computation on multi core or GPU (Graphics Processing Unit) architecture. Some of this work has already been conducted by the research group at Narvik University College. Since 2004 Narvik University College has been conducting research on diﬀerent topics within this area. Such as wavelet shrinkage: curve and surface ﬁtting, [8], and strategies, [3]. Visualization and isometric conversion between dimension and resolution, [4, 5, 7]. As well as computing n-variate orthogonal discrete wavelet transforms on graphics processing units, [6]. Discrete wavelet transforms, operations on the transformed data and packing algorithms are usually found at the core of most compression algorithms today. In an upcoming publication our latest eﬀorts investigating prerequisites for these algorithms have brought us some interesting new ways of looking at data structures. An algorithm for indexing blocks of tensor-product wavelet bases has been proposed in [6], and in [2] the algorithm was extended to directly index the individual bases inside each of the basis-blocks. In theory this allows for computation of an ndimensional discrete wavelet transform of an n-dimensional data set to be performed in one dimension. The case of I = 1d 7→ nd and the inverse I −1 7→ 1d. Then in our latest eﬀorts an extended algorithm building the aforementioned algorithm once more were built for the general case, so one can map basis functions between any two dimensions, Id1 7→d2 . Using a one dimensional DWT (Discrete Wavelet Transform) to compute a higher dimensional DWT does not only make the implementation easier by giving us a uniﬁed framework for such computations, but some expectations about using them as operators on data sets that otherwise would not have been seen as easily streamable, will fall. 3 Data approximation and compression This is the start of a three year long PhD project which will be ﬁnalized in the beginning of 2014. An Narvik University College in-house open source geometry and graphics library, GMlib [1], is planned to be extensively utilized and extended for prototyping during this project. The library has been developed as a collection of geometric modeling, graphics and computer science tools and algorithms since 1994, and are now available as an open source library on the Episteme [1] websites of Narvik University College. Some tasks is also considered scheduled in the near future. GPU implementation of a multivariate indexation algorithm is already at a prototype stage with code for generating example illustration utilizing OpenCL. This will most likely also be extended under diﬀerent programming paradigms, as CUDA (NVidia) and Direct Compute (Microsoft). Prototype implementations for other compression test cases as JPEG2000 and H.264 are also planned, and will most likely generate considerable work. A ﬁrst Funcom pipeline-ready prototype of a working tool based on ”Methods for user guided compression algorithms” is not expected until late 2012. Pre and post processing and approximation of data on sinal level using for example Exporational B-splines is somewhat linked to the Funcom pipeline-ready prototype and will be investigated in parallel. Figure 1: In the top left the traditional ”Lena” benchmark can be seen, and below it at the left side a one dimensional representation of the same image. This one dimensional set have been put through a three level one dimensional forward DWT, and at the bottom right you can see the one dimensional representation of the result. When this result is indexed as in [6], we can put it together again as seen in the top right of the picture, which coincides with the output of a traditional two dimensional forward DWT. References [1] J. Bratlie. Episteme, home of gmlib http://episteme.hin.no, July 2011. [2] L. Dechevsky, J. Bratlie, and J. Gundersen. Index mapping between tensor-product wavelet bases of diﬀerent number of variables, and computing multivariate orthogonal discrete wavelet transforms on graphics processing units. In Large-Scale Scientific Computing, Lecture Notes in Computer Science, 2011. LSSC 2011, Sozopol, Bulgaria. Avaiting review. [3] L. T. Dechevsky, N. Grip, and J. Gundersen. A new generation of wavelet shrinkage: adaptive strategies based on composition of lorentz-type threshold and besov-type non-threshold shrinkage. In F. Truchetet and O. Laligant, editors, Wavelet applications in industrial processing V, Optics East 2007, 2007. Volume 6763 of Proceedings of SPIE. Bellingham, Washington 2007. Paper 676304. [4] L. T. Dechevsky and J. Gundersen. From dynamical visualization of large 3d and 4d geometrical data sets of isometric conversion between dimension and resolution. In Large-Scale Scientific Computing, volume 3 of Preprint, page 21. Narvik University College, 2004. ISSN 1504-4653. [5] L. T. Dechevsky and J. Gundersen. Isometric conversion between dimension and resolution. In M. D. ælen, K. M. rken, and L. Schumaker, editors, Mathematical methods for Curves and Surfaces, pages 103–114, 2005. [6] L. T. Dechevsky, J. Gundersen, and B. Bang. Computing n-variate orthogonal discrete wavelet transforms on graphics processing units. In I. Lirkov, S. Margenov, and J. Wasniewski, editors, Large-Scale Scientific Computing, volume 5910 of Lecture Notes in Computer Science, pages 730–737. Springer Berlin / Heidelberg, 2010. 10.1007/978-3-642-12535-5 87. [7] L. T. Dechevsky, J. Gundersen, and A. R. Kristoﬀersen. Wavelet-based isometric conversion between dimension and resolution and some of its applications. In F. Truchetet and O. Laligant, editors, Wavelet applications in industrial processing V, Optics East 2007, 2007. Volume 6763 of Proceedings of SPIE. Bellingham, Washington 2007. Paper 67630Q. [8] T. Moguchaya, J. Gundersen, N. Grip, L. Dechevsky, B. Bang, A. Lakså, E. Quak, and B. Tong. Curve and surface ﬁtting by wavelet shrinkage using gm-waves. In M. Dælen, K. Mørken, and L. Schumaker, editors, Mathematical methods for Curves and Surfaces, pages 263–274, 2005.