Abstract
In this paper we introduce a novel GPU-supported JPEG image compression technique with a focus on its application for remote visualization purposes. Fast and high quality compression techniques are very important for the remote visualization of interactive simulations and Virtual reality applications (IS/VR) on hybrid clusters. Thus the main goals of the design and implementation of this compression technique were low compression times and nearly no visible quality loss, while achieving compression rates that allow for 30+ Frames per second over 10 MBit/s networks. To analyze the potential of the technique and further development needs and to compare it to existing methods, several benchmarks are conducted and described in this paper. Additionally a quality assessment is performed to allow statements about the achievable quality of the lossy image compression. The results show that using the GPU not only for rendering but also for image compression is a promising approach for interactive remote rendering.
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References
Lietsch, S., Zabel, H., Berssenbruegge, J.: Computational Steering of Interactive and Distributed Virtual Reality Applications. In: ASME CIE 2007: Proceedings of the 27th ASME Computers and Information in Engineering Conference, ASME (2007)
Lietsch, S., Marquardt, O.: A CUDA-Supported Approach to Remote Rendering. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part I. LNCS, vol. 4841, pp. 724–733. Springer, Heidelberg (2007)
VirtualGL: The VirtualGL Project (2007), http://www.virtualgl.org/
Independent JPEG Group: libjpeg (Open Source JPEG library) (2008), http://www.ijg.org
VirtualGL: TurboJPEG 1.10 - Intel IPP accelerated JPEG compression (2008), http://www.sourceforge.net /project/showfiles.php?group_id=117509&package_id=166100
Intel: Intel Integrated Performance Primitives 5.3 (2008), http://www.intel.com /cd/software/products/asmo-na/eng/302910.htm
NVIDIA: NVIDIA CUDA - Compute Unified Device Architecture (2008), http://www.nvidia.com/object/cuda_home.html
Pennebaker, W.B., Mitchell, J.L.: JPEG Still Image Data Compression Standard. Kluwer Academic Publishers, Norwell (1992)
Arai, Y., Agui, T., Nakajima, M.: A Fast DCT-SQ Scheme for Images. Transactions of IEICE E71, 1095–1097 (1988)
Howard, P.G., Vitter, J.S.: Parallel lossless image compression using Huffman and arithmetic coding. Information Processing Letters 59, 65–73 (1996)
Crochemore, M., Wojciech, W.R.: Jewels of stringology. World Scientific Publishing Co. Inc., River Edge (2003)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)
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Lietsch, S., Lensing, P.H. (2008). GPU-Supported Image Compression for Remote Visualization – Realization and Benchmarking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_63
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DOI: https://doi.org/10.1007/978-3-540-89639-5_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89638-8
Online ISBN: 978-3-540-89639-5
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