Dynamic load balancing in image retargeting using pipeline architecture

Ganesh V. Patil, Santosh L. Deshpande

Abstract


In today’s smart world demand of efficient multimedia based communication has increased at a rapid rate. Diversity on display sizes of gadgets used for multimedia communication confines the quality of images. Image retargeting is used as the focal solution to this problem which results in images with appropriate sizes. Enormously mounting demand of image retargeting expedites the rate of increment in computational load. This research paper expatiate and experiments a dynamic load balancing based three phase image retargeting methodology using pipeline architecture. In the first phase of image retargeting resize operation is performed on input image which results in multiple sized image copies of the same image. In the second phase resized images undergo quantization operation. In the final phase lossless compression is performed to have an expedient image. In the proposed exhibit think, we have done statistical analysis of results obtained, to confirm an impartial dynamic load balancing with a better degree of underlying resource utilization. We extend the approach to achieve significant storage optimization using three phase image retargeting.

Full Text:

PDF

References


Ganesh Patil,Santosh Deshpande, “Image optimisation using dynamic load balancing”, International Journal of Knowledge Engineering and Data Mining, Vol. 5, Nos. 1/2, pp.68-89, 2018.

Sumai Khan, Babar Nazir, Iftikhar Ahmed Khan, Shahaboddin Shashirband and Anthony T Chronopoulos, "Load Balancing in Grid Computing: Taxonomy, Trends and Opportunities," Journal of Network and Computer Applications, Accepted on 20 February 2017.

Shang-Liang Chen, Yun-Yao Chen, Suang-Hong Kuo, "CLB: A novel load balancing architecture and algorithm for cloud services,” Computers and Electrical Engineering, pp. 1-7, Accepted on 12 January, 2016.

Bokhari, M.U., Alam, M., Hasan, F.,“Performance Analysis of Dynamic Load Balancing Algorithm for Multiprocessor Interconnection Network,” Perspectives in Science, pp. 1-5, June 2016.

Patil, G. and Deshpande, S.L., “Distributed rendering system for 3D animation with blender,” Presented at IEEE Sponsored International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT 2016), pp.92–98 , December 2016.

Bin Zhou , Xuanyin Wang , Songxiao Cao , Ke Xiang , Shuo Zhao, “Optimal bi-directional seam carving for compressibility-aware image retargeting, ” J. Vis. Commun. Image R., September 2016.

YanxiangChen,YifeiPan,MinglongSong, MengWang, “Improved seam carving combining with 3D saliency for image retargeting,” Neurocomputing 151, pp. 645–653, October 2014.

Tali Dekel (Basha),Yael Moses, Shai Avidan, “Stereo Seam Carving a Geometrically Consistent Approach,” IEEE Transactions on Pattern analysis and machine intelligence , Vol. 35 , No.10 ,pp. 2513-2525, October 2013.

Bin Dong, Xiuqiao Li, Qimeng Wu, Limin Xiao, Li Ruan “A dynamic and adaptive load balancing strategy for parallel file system with large-scale I/O servers,” J. Parallel Distrib. Comput. 72 (2012) pp. 1254–1268, May 2012.

Yuming Fang, Zhenzhong Chen, Weisi Lin, Chia-Wen Lin, “Saliency Detection in the Compressed Domain for Adaptive Image Retargeting,” IEEE Transactions on Image Processing, Vol. 21, No. 9, pp-3888-3901, September 2012.

Doaa M. Abdelkader, Fatma Omara, “Dynamic task scheduling algorithm with load balancing for heterogeneous computing system,” Egyptian Informatics Journal, pp. 135–145, July 2012.

Qingqi Long, Jie Lin, Zhixun Sun, “Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations, ” Simulation Modelling Practice and Theory 19, pp. 1021–1034, January 2011.

Joni-Matti Maatta, Jarno Vanne, Timo D. Hamalainen, and Jarno Nikkanen “Generic Software Framework for a Line-Buffer-Based Image Processing Pipeline” IEEE Transactions on Consumer Electronics, Vol. 57, No. 3, pp. 1442-1449, August 2011.

Satish Penmatsa, Anthony T. Chronopoulos “Game-theoretic static load balancing for distributed systems,” J. Parallel Distrib. Comput. 71 , pp. 537–555, 2011, December 2010.

Jin Sun, Haibin Ling “Scale and Object Aware Image Retargeting for Thumbnail Browsing,”Presented at IEEE International Conference on Computer Vision, pp.1511-1518, November 2011.

Jun-Seong Kim, Jin-Hwan Kim, and Chang-Su Kim“Adaptive Image and Video Retargeting Technique Based on Fourier Analysis,” Presented at IEEE Conference on Computer Vision and Pattern Recognition, pp.1730-1737, June 2009.

Jin-Hwan Kim, Jun-Seong Kim, and Chang-Su Kim “Image and Video Retargeting using Adaptive Scaling Function” 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland, pp.819-823, August 24-28, 2009.

Shu-Fan Wang and Shang-Hong Lai “Fast Structure Preserving Image Retargeting ” Presented at International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1049-1052, April 2009.

Kang-Sun Choi, Sung-Jea Ko, “Fast Content-Aware Image Resizing Scheme in the Compressed Domain” IEEE Transactions on Consumer Electronics, Vol. 55, No. 3, August 2009.

CHEN Wei-dong, DING Wei “An Improved Median-cut Algorithm of Color Image Quantization” IEEE International Conference on Computer Science and Software Engineering, pp.943-946, December 2008.

Grosu, D. and Chronopoulos, A.T., “Algorithmic mechanism design for load balancing in distributed systems,” IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics,Vol. 34, No. 1, pp.77–84, February 2004.

David E. Rex, Jeffrey Q. Ma, and Arthur W. Toga* “The LONI Pipeline Processing Environment” NeuroImage 19, pp.1033-1048, 18 March 2003.

Danial Grosue,Anthony T. Chronopoulos, Ming-Ying Leung (2002), “Load balancing in distributed systems: A game theoretic approach”, Presented at IEEE International Parallel and Distributed Processing Symposium (IPDPS 2002), pp.501-510, April 2002.

Zomaya, A.Y. and Teh, Y-H., “Observations on using genetic algorithms for dynamic load-balancing,” IEEE Trans. on Parallel and Distributed Systems, , Vol. 12, No. 9, pp.899–911, September 2001.

Jaspal Subhlok, Gary Vondran, “Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations ” Journal of Parallel and Distributed Computing Vol. 60, pp.297-319, March 2000.

Watts, J. and Taylor, S., “A practical approach to dynamic load balancing,” IEEE Trans. on Parallel and Distributed Systems, March, Vol. 9, No. 3, pp.235–248, March 1998.

Willebeek-LeMair, M.H. and Reeves, A.P., “Strategies for dynamic load balancing on highly parallel computer,” IEEE Trans. on Parallel and Distributed Systems, Vol. 4, No. 9, pp.979–993 September 1993.

Lin, H-C. and Raghavendra, C.S., “A dynamic load-balancing policy with a central job dispatcher (LBC),” IEEE Trans. on Software Engineering, , Vol. 18, No. 2, pp.148–158, February 1992.

Chow, Y-C. and Kohler, W.H. “Models for dynamic load balancing in a heterogeneous multiple processor system,” IEEE Trans. on Computers, Vol. C-28, No. 5, pp.354–361,May 1979.

World Wide Survey, https://mylio.com/true-stories/tech-today/how-many-digital-photos-will-be-taken-2017-repost accessed on (12th March,2018 )

Image Processing available at: http://interactivepython.org/runestone/static/thinkcspy/MoreAboutIteration/2DimensionalIterationImageProcessing.html (accessed on 2nd Nov, 2017)

Michael Still “The Definitive Guide to ImageMagick” Apress, 2006.

Image Quantization available at https://en.wikipedia.org/wiki/Quantization_(image_processing) (accessed on 6th Nov,2017)

Advpng available at http://www.advancemame.it/doc-advpng.html(accessed on 6th Nov,2017)

Grafana available at https://grafana.com(accessed on 6th Nov,2017)

Pngquant available at https://pngquant.org(accessed on 6th Nov,2017)




DOI: http://dx.doi.org/10.21533/pen.v6i2.270

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Ganesh V

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License