Abstract
Along with the extension of the application of the dictionary learned through the synthesis model in the image compression, the time consumption in the sparse representation becomes a key factor restricting the efficiency of the system. Therefore in view of the defect of the synthesis model in the application, combining with the advantages of the analysis model in the sparse representation, we proposed an image block compression model based on analysis dictionary (ALDBCS). In this model, a dictionary which is obtained by using the prior data, is introduced to the process of image compression. The reconstructed simulation experiment proves that the ALDBCS model can not only improve the quality of image reconstruction, but also reduce the consumption of image compression.
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References
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theor. 52(4), 1289–1306 (2006)
Candes, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theor. 52(2), 489–509 (2006)
Jiao, L., Yang, S., Liu, F., Hou, B.: Review and prospect of compressed sensing. Chin. J. Electron. 39(7), 165–1662 (2011)
Lian, Q., Shi, B., Chen, S.: Progress in research on dictionary learning models, algorithms and applications. Acta Automatica Sin. 41(2), 240–260 (2015)
Michal, E., Milanfar, P., Rubinstein, R.: Analysis versus synthesis in signal priors. Inverse Probl. 23(3), 947–968 (2007)
Rubinstein, R., Bruckstein, A.M., Michal, E.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045–1057 (2010)
Nam, S., Davies, M.E., Michal, E., Gribonval, R.: The cosparse analysis model and algorithms. Appl. Comput. Harmonic Anal. 34(1), 3–56 (2013)
Rubinstein, R., Michal, E.: K-SVD dictionary-learning for analysis sparse models. IEEE Int. Conf. Acoust. Speech Signal Process. 22(10), 540–5408 (2012)
Rubinstein, R., Peleg, T., Michal, E.: Analysis K-SVD: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans. Signal Process. 61(3), 661–677 (2013)
Rubinstein, R., Michal, E.: Dictionary learning for analysis-synthesis thresholding. IEEE Trans. Signal Process. 62(22), 5962–5972 (2014)
Ring, W., Wirth, B.: Optimization methods on riemannian manifolds and their application to shape space. Soc. Indian Autom. Manuf. J. Optimi. 22(2), 596–627 (2012)
Simon, H., Martin, K., Klaus, D.: Analysis operator learning and its application to image reconstruction. IEEE Trans. Image Process. 22(6), 2138–2150 (2013)
Dong, J., Wang, W., Dai, W.: Analysis SIMCO: a new algorithm for analysis dictionary learning. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), pp. 7193–7197 (2014)
Dong, J., Wang, W., Dai, W., Plumbley, M.D., Han, Z., Chambers, J.: Analysis SimCO algorithms for sparse analysis model based dictionary learning. IEEE Trans. Signal Process. 64(2), 417–431 (2016)
Li, Y., Ding, S., Li, Z.: A dictionary-learning algorithm for the analysis sparse model with a determinant-type of sparsity measure. In: Proceeding of the International Conference on Digital Signal Processing, pp. 20–23 (2014)
Kiechle, K., Habigt, T., Simon, H.: Martin kleinsteuber.a bimodal cosparse analysis model for image processing. Int. J. Comput. Vis. 114(2), 33–247 (2015)
Michal, A., Michal, E., Alfred, M.B.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Acknowledgement
This work has been partially supported by the National Natural Science Foundation of China (Grant No. 61572372 and 41271398), LIESMARS Special Research Funding, and also partially supported by the Fund of SAST (Project No. SAST201425). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Feng, Z., Chong, Y., Zheng, W., Pan, S., Guo, Y. (2016). Image Compression Based on Analysis Dictionary. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_40
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DOI: https://doi.org/10.1007/978-3-319-42294-7_40
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