ارائه یک روش جدید فشرده سازی تصاویر چهره با استفاده از نمایش تنک سیگنال و الگوریتم یادگیری دیکشنری RLS-DLA
محورهای موضوعی : مهندسی الکترونیک
امیرمسعود طاهری
1
(دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
همایون مهدوی نسب
2
(مرکز تحقیقات پردازش دیجیتال و بینایی ماشین، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
کلید واژه: Image compression, نمایش تُنک, کدگذاری تُنک, یادگیری دیکشنری, RLS-DLA, JPEG2000, sparse representation, sparse coding, dictionary learning, فشرده سازی تصویر,
چکیده مقاله :
رشد فناوری و افزایش تصاعدی اطلاعات نیاز به حجم ذخیره سازی بیشتر داده های اطلاعاتی گوناگون را افزایش داده است. در این راستا فشرده سازی تصویر به‌عنوان ابزاری کارآمد جهت کاهش افزونگی و صرفه جویی در حجم ذخیره سازی و کاهش پهنای باند انتقالی داده های تصویری به کار می آید. هنگامی که فشرده سازی یک دسته یا خانواده از تصاویر، مانند پایگاه داده تصاویر چهره یک سازمان یا مؤسسه یا پایگاه داده MRI یک بیمارستان بزرگ یا پایگاه داده اثر انگشت مدنظر باشد افزونگی اطلاعات افزایش یافته و فشرده سازی اهمیت و الزام بیشتری پیدا می کند. در این میان تصاویر چهره با توجه به کاربرد وسیعی که به‌عنوان رایج ترین تصاویر پایگاه داده سازمان‌های و نهادهای مختلف مانند اداره های پلیس، نهادهای نظامی، دانشگاه ها و شرکت های بزرگ دارند مورد توجه بیشتری قرار دارند. به همین خاطر ارائه الگوریتمی که بتواند این دسته از تصاویر را با کیفیت بیشتر و نرخ بالاتری فشرده کند اهمیت بسیاری دارد. در این مقاله با استفاده از حوزة جدیدی از پردازش سیگنال به نام نمایش تُنک و روش یادگیری دیکشنری RLS-DLA الگوریتم جدیدی برای فشرده سازی تصویر ارائه شده است که می تواند برای فشرده سازی پایگاه داده تصاویر به کار رود. در این الگوریتم تصاویر با به‌کارگیری چند دیکشنری به نحو وفقی بر اساس کیفیت بازسازی مورد نیاز آن‌ها فشرده می شوند. نتایج به‌دست‌آمده از الگوریتم پیشنهادی نشان‌دهنده عملکرد مؤثر و برتری معنی دار آن نسبت به روش‌های پیشرفته و مطرحی همچون JPEG2000 است به‌طوری‌که به افزایش کیفیتی در حدود 0.5 dB تا 1.2 dB در نرخ بیت یکسان دست می یابد.
Due to the rapid growth of information technology and exponential increasing of information the need for more and more storage capacity and efficiency has increased. Image compression is an important tool to reduce the redundancy of images data in order to be able to store or transmit them in an efficient manner. When images are limited to a specific and limited family of images like MRI databases of a hospital or facial image database of a university or an organization or fingerprint image databases, this limitation increases the total spatial redundancy. Thus, efficient storage of such images is beneficial, and their compression becomes an appealing application, and this urges algorithms specially tailored for the task of content base image compression to surpass general purpose compression algorithms. The facial images, due to their wide application as the most common images in the organizations and companies are more considerable for image compression. In this paper a new image compression scheme using sparse coding and RLS-DLA redundant dictionary learning is proposed that can be used for compressing of face image databases. In the proposed method, several dictionaries are exploited adaptively based on the required image quality to enhance the overall rate-distortion. The simulation results show that this scheme outperforms the state-of-art algorithms like JPEG2000 by about 0.5 to 1.2 dB for reconstructed images PSNR.
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[10] G. Shao, Y. Wu, X. Liu, and T. Guo, “Fingerprint Compression Based on Sparse Representation,” Image Process. IEEE Trans., vol. 23, no. 2, pp. 489–501, 2014.
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[14] W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” Image Process. IEEE Trans., vol. 20, no. 7, pp. 1838–1857, 2011.
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[29] R. Rubinstein, T. Peleg, and M. Elad, “Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model,” IEEE Trans. Signal Process., vol. 61, pp. 661–677, 2013.
[30] K. Skretting and K. Engan, “Recursive least squares dictionary learning algorithm,” Signal Process. IEEE Trans., vol. 58, no. 4, pp. 2121–2130, 2010.
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_||_
[1] M. Elad, “Sparse and redundant representation modeling-What next?,” IEEE Signal Process. Lett., vol. 19, pp. 922–928, 2012.
[2] M. Elad, M. A. T. Figueiredo, and Y. Ma, “On the role of sparse and redundant representations in image processing,” Proc. IEEE, vol. 98, pp. 972–982, 2010.
[3] M. Elad, Sparse and redundant representations: From theory to applications in signal and image processing. 2010.
[4] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, pp. 210–227, 2009.
[5] J. Yang, J. Wright, T. Huang, and Y. Ma, “Image Super-Resolution via Sparse Representation.,” IEEE Trans. Image Process., vol. 19, pp. 2861–2873, 2010.
[6] K. Skretting and K. Engan, “Image compression using learned dictionaries by RLS-DLA and compared with K-SVD,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., pp. 1517–1520, 2011.
[7] O. Bryt and M. Elad, “Compression of facial images using the K-SVD algorithm,” J. Vis. Commun. Image Represent., vol. 19, no. 4, pp. 270–282, 2008.
[8] J.-Y. Zhu, Z.-Y. Wang, R. Zhong, and S.-M. Qu, “Dictionary based surveillance image compression,” J. Vis. Commun. Image Represent., vol. 31, pp. 225–230, 2015.
[9] I. Horev, O. Bryt, and R. Rubinstein, “Adaptive image compression using sparse dictionaries,” in Systems, Signals and Image Processing (IWSSIP), 2012 19th International Conference on, 2012, pp. 592–595.
[10] G. Shao, Y. Wu, X. Liu, and T. Guo, “Fingerprint Compression Based on Sparse Representation,” Image Process. IEEE Trans., vol. 23, no. 2, pp. 489–501, 2014.
[11] J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 2009, pp. 1794–1801.
[12] X. Mei and H. Ling, “Robust visual tracking and vehicle classification via sparse representation,” Pattern Anal. Mach. Intell. IEEE Trans., vol. 33, no. 11, pp. 2259–2272, 2011.
[13] J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” Image Process. IEEE Trans., vol. 17, no. 1, pp. 53–69, 2008.
[14] W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” Image Process. IEEE Trans., vol. 20, no. 7, pp. 1838–1857, 2011.
[15] Y. Tsaig and D. L. Donoho, “Extensions of compressed sensing,” Signal Processing, vol. 86, no. 3, pp. 549–571, 2006.
[16] D. L. Donoho, “Compressed sensing,” Inf. Theory, IEEE Trans., vol. 52, no. 4, pp. 1289–1306, 2006.
[17] S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” Signal Process. IEEE Trans., vol. 53, no. 7, pp. 2477–2488, 2005.
[18] J. A. Tropp and S. J. Wright, “Computational methods for sparse solution of linear inverse problems,” Proc. IEEE, vol. 98, pp. 948–958, 2010.
[19] R. Rubinstein, A. M. Bruckstein, and M. Elad, “Dictionaries for sparse representation modeling,” Proc. IEEE, vol. 98, pp. 1045–1057, 2010.
[20] Z. Zhang, Y. Xu, J. Yang, X. Li, and D. Zhang, “A survey of sparse representation: algorithms and applications,” Access, IEEE, vol. 3, pp. 490–530, 2015.
[21] E. Amaldi and V. Kann, “On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems,” Theor. Comput. Sci., vol. 209, no. 1, pp. 237–260, 1998.
[22] J. A. Tropp, “Greed is good: Algorithmic results for sparse approximation,” IEEE Trans. Inf. Theory, vol. 50, pp. 2231–2242, 2004.
[23] A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev., vol. 51, no. 1, pp. 34–81, 2009.
[24] M. Gharavi-Alkhansari and T. S. Huang, “A fast orthogonal matching pursuit algorithm,” in Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on, 1998, vol. 3, pp. 1389–1392.
[25] E. Liu and V. N. Temlyakov, “The orthogonal super greedy algorithm and applications in compressed sensing,” Inf. Theory, IEEE Trans., vol. 58, no. 4, pp. 2040–2047, 2012.
[26] I. Tošić and P. Frossard, “Dictionary learning,” Signal Process. Mag. IEEE, vol. 28, no. 2, pp. 27–38, 2011.
[27] K. Engan, S. O. Aase, and J. H. Husoy, “Method of optimal directions for frame design,” 1999 IEEE Int. Conf. Acoust. Speech, Signal Process. Proceedings. ICASSP99 (Cat. No.99CH36258), vol. 5, 1999.
[28] M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process., vol. 54, pp. 4311–4322, 2006.
[29] R. Rubinstein, T. Peleg, and M. Elad, “Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model,” IEEE Trans. Signal Process., vol. 61, pp. 661–677, 2013.
[30] K. Skretting and K. Engan, “Recursive least squares dictionary learning algorithm,” Signal Process. IEEE Trans., vol. 58, no. 4, pp. 2121–2130, 2010.
[31] D. Taubman and M. Marcellin, JPEG2000 Image Compression Fundamentals, Standards and Practice: Image Compression Fundamentals, Standards and Practice, vol. 642. Springer Science & Business Media, 2012.
[32] Y. Q. Shi and H. Sun, “Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards,” 2008.
[33] J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online dictionary learning for sparse coding,” in Proceedings of the 26th annual international conference on machine learning, 2009, pp. 689–696.
