Dictionary Based Intra Prediction for Image Compression

• Autorzy: Sahoo Arabinda , Das Pranati

Identyfikatory

DOI

10.15439/2017R17

• Konferencja: The Second International Conference on Research in Intelligent and Computing in Engineering
• Języki publikacji: EN

Abstrakty

EN

Recently image coding has been importantand in many field it is necessary. Recently varioussparse algorithms have been developed for imagecompression. This paper presents a dictionary basedblock intra prediction for image compression withconstruction of an adaptive trained dictionary. Theadaptive trained dictionary is prepared using K-SVDalgorithm. K-SVD algorithm update the dictionarybased on the sparse algorithms and given image. Theprediction residuals selected from different image areused for dictionary training. The orthogonal matchingpursuit (OMP) algorithm have employed for selectionof dictionary elements and encoding. The proposedmethod is then integrated into 9 mode H.264 intracoding. Performance of proposed method comparedwith existing methods. Simulation result shows thatproposed scheme has improved efficiency as comparedto existing schemes.

Słowa kluczowe

EN

sparse coding; matching pursuit; orthogonal matching pursuit; K-SVD

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2017
• Tom: Vol. 10
• Strony: 73--76
• Opis fizyczny: Bibliogr. 21 poz., rys., wykr.

Twórcy

autor

Sahoo Arabinda

• Arabinda Sahoo Department of Electronics and Communication Engineering, ITER, Siksha ‘O’ Anusandhan University Bhubaneswar, Odisha, India

autor

Das Pranati

• Department of Electrical Engineering IGIT, Sarang, Odisha, India

Bibliografia

• 1. D. Hankerson, G. A. Harris, P. D. Johnson Jr., Introductionto Information Theory and Data Compression, CRC Press, BocaRaton, FL, 1997.
• 2. O. Egger, P. Fleury, T. Ebrahimi, M. Kunt, High performance compression of visual information a tutorial review – part I: still pictures, Proc. IEEE 87, 1999.
• 3. V. P. Baligar, L. M. Patnaik, G. R. Nagabhushana, High compression and low order linear predictor for lossless coding of grayscale images, Image Vis. Comput. 21 (6), 2003.
• 4. D. Salomon, Data Compression: The Complete Reference, third ed., Springer, New York, 2004.
• 5. D. Salomon, A Guide to Data Compression Methods, Springer, New York, 2002.
• 6. K. Sayood, Introduction to Data Compression, second ed., Academic Press, San Diego, CA, 2000.
• 7. G. K. Wallace, ―The JPEG still picture compression standard, IEEE transactions on Consumer Electronics,Dec. 1991.
• 8. ITU-T Rec. T.800 | ISO/IEC 15444-1, ―InformationTechnology — JPEG 2000 Image Coding System — Part 1: Core Coding System, 2001.
• 9. G. Rath and A. Sahoo,―A comparative study of some greedy pursuit algorithms for sparse approximation, EUSIPCO2009, pp. 398-402, Aug. 2011.
• 10. S. Mallat, Z. Zhang, ―Matching pursuits with time frequency dictionaries, IEEE Transactions on Signal Processing vol. 41, no. 12, pp. 3397-3415. 1993.
• 11. Y. C. Pati, R. Rezaiifarand, P. S. Krishnaprasad.,―Orthogonal Matching pursuit: Recursive function approximation with application to wavelet decomposition, In proc. 27th conf. on sig. sys. and comp., vol.1, Nov. 1993.
• 12. G. Rath., and C. Guillemot, ―Complimentary Matching pursuits Algorithms for sparse Approximation, Journal of signal processing, pp.702-706, Jan. 2009.
• 13. O. Bryt and M. Elad, ―Compression of facial images using the K-SVD algorithm, J. Vis. Commun. Image Representation. vol. 19, no. 4, pp. 270-282, 2008.
• 14. Yuan Yuan, Oscar C. Au, Amin Zheng, Haitao Yang, Ketan Tang and Wenxiu Sun, Image compression via sparse reconstruction, 2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014.
• 15. I. Horev, O. Bryt, R. Rubinstein, Adaptive image compression using sparse dictionaries, in: Proc. of IWSSIP, pp. 592-595, 2012.
• 16. Jing-Ya Zhu, Zhong-Yuan Wang , Rui Zhong, Shen-Ming Qu, Dictionary based surveillance image compression, J. Vis. Commun. Image R. 31 (2015) 225-230, 2015.
• 17. M. Aharon, M. Elad, A. Bruckstein, K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation, IEEE Trans. Signal Process. 54 (2006) 4311-4322, 2006.
• 18. J. Zepeda, C. Guillemot, E. Kijak, Image compression using sparse representations and the iteration tuned and aligned dictionary,IEEE Journal of Selected Topics in Signal Processing, vol. 5(5), pp. 1061-1073, 2011.
• 19. Mehmet Türkan, Christine Guillemot, Dictionary learning for image prediction, J. Vis. Commun. Image R. 24 (2013) 426-437, 2013.
• 20. Yun Song, Wei Cao, Yanfei Shen, Gaobo Yang, Compressed sensing image reconstruction using intra prediction, Neurocomputing 151(2015)1171-1179, 2015. https://doi.org/10.1016/j.neucom.2014.05.088
• 21. Turkan, M., and Guillemot, C., Sparse Approximation With Adaptive Dictionary for Image Prediction,In proc.international conference on image processing (ICIP), 2010.