Open Access Open Access  Restricted Access Subscription Access

A New Image Compression by Gradient Haar Wavelet


Affiliations
1 Department of Computer Sciences, Shandiz Institute of Higher Education, Mashhad, Iran, Islamic Republic of

   Subscribe/Renew Journal


With the development of human communications, the usage of visual communications has also increased. The advancement of image compression methods is one of the main reasons for the enhancement. This paper first presents main modes of image compression methods such as JPEG and JPEG2000 without mathematical details. Also, the paper describes gradient Haar wavelet transforms in order to construct a priliminary image compression algorithm so that sub images inherit the same amount of original image information. Then, a new image compression method is proposed based on the preliminary image compression algorithm that can improve standards of image compression. The new method is compared with original modes of JPEG and JPEG2000 (based on Haar wavelet) by image quality measures such as MAE, PSNAR, and SSIM. The image quality and statistical results confirm that can boost image compression standards. It is suggested that the new method is used in a part or all of an image compression standard.

Keywords

Digital Images, Image Communication, Image Decomposition, Image Storage, Image Quality, Wavelet.
User
Subscription Login to verify subscription
Notifications
Font Size

  • M. N. Do, D. H. Nguyen, H. T. Nguyen,D. Kubacki, and S. J. Patel, "Immersive visual communication," IEEE Signal Process. Mag., vol. 28, no. 1, pp. 58-66, 2011. https://dx.doi.org/10.1109/MSP.2010.939075
  • F.Yang and S. Wan, "Bitstream-based quality assessment for networked video: A review," IEEE Commun. Mag.,, vol. 50, no. 11, pp. 203-209, 2012. https://dx.doi.org/10.1109/MCOM.2012.6353702
  • A. Borji and L. Itti, "State-of-the-art in visual attention modeling," IEEE Trans. on Pattern Anal. and Mach. Intell., vol. 35, no. 1, pp. 185-207, 2013. https://dx.doi.org/10.1109/TPAMI.2012.89
  • K. Ma, H. Yeganeh, K. Zeng and Z. Wang, "High dynamic range image compression by optimizing tone mapped image quality index," in IEEE Trans. on Image Process., vol. 24, no. 10, pp. 3086-3097, Oct. 2015.
  • A. Taneja, L. Ballan and M. Pollefeys, "Geometric change detection in urban environments using images," in IEEE Trans. on Pattern Analysis and Mach. Intell., vol. 37, no. 11, pp. 2193-2206, 2015. https://dx.doi.org/10.1109/TPAMI.2015.2404834
  • H. Nejati, V. Pomponiu, T. T. Do, Y. Zhou, S. Iravani, and N. M. Cheung, "Smartphone and mobile image processing for assisted living: Health-monitoring apps powered by advanced mobile imaging algorithms," in IEEE Signal Process. Mag., vol. 33, no. 4, pp. 30-48, July 2016. https://dx.doi.org/10.1109/MSP.2016.2549996
  • R. Wang et al., "The MPEG internet video-coding standard [Standards in a Nutshell]," in IEEE Signal Process. Mag., vol. 33, no. 5, pp. 164-172, 2016. Doi: https://dx.doi.org/10.1109/MSP.2016.2571440
  • J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, "Image denoising using scale mixtures of Gaussians in the wavelet domain," in IEEE Trans. on Image Process., vol. 12, no. 11, pp. 1338-1351, 2003. https://dx.doi.org/10.1109/TIP.2003.818640
  • Y. Kwon, K. I. Kim, J. Tompkin, J. H. Kim, and C. Theobalt, "Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes," in IEEE Trans. on Pattern Analysis and Mach. Intell., vol. 37, no. 9, pp. 1792-1805, 2015. https://dx.doi.org/10.1109/TPAMI.2015.2389797
  • B. E. Usevitch, "A tutorial on modern lossy wavelet image compression: Foundations of JPEG 2000," IEEE Signal Process. Mag., vol. 18, no. 5, pp. 22-35, Sep 2001. https://dx.doi.org/10.1109/79.952803
  • J. Zumberge, L. Deutsch and S. Townes, "Deep space communications," in JPL Publication 400-1385. Jet Propulsion Laboratory, California Inst. of Technol. (Pasadena, CA), 2017. [Online]. Available: https://scienceandtechnology.jpl.nasa.gov/research/research-topics-list/communications-computing-software/deep-space-communications
  • A. M. Rufai, G. Anbarjafari, ands H. Demirel, "Lossy image compression using singular value decomposition and wavelet difference reduction," in Digital Signal Process., vol. 24, pp. 117-123, 2014. https://dx.doi.org/10.1016/j.dsp.2013.09.008
  • N. Goel and S. Gabarda, "Lossy and lossless image compression using Legendre polynomials," in 2016 Conf. on Advances in Signal Process. (CASP), Pune, pp. 315-320, 2016. https://dx.doi.org/10.1109/CASP.2016.7746187
  • F. Sheng, A. Bilgin, P. J. Sementilli and M. W. Marcelling, "Lossy and lossless image compression using reversible integer wavelet transforms," Proc. 1998 Int. Conf. on Image Process. ICIP98 (Cat. No.98CB36269), Chicago, IL, vol 3., pp. 876-880, 1998. https://dx.doi.org/10.1109/ICIP.1998.727392
  • C. Lan, J. Xu, W. Zeng and F. Wu, "Compound image compression using lossless and lossy LZMA in HEVC," 2015 IEEE Int. Conf. on Multimedia and Expo (ICME), Turin, pp. 1-6, 2015. https://dx.doi.org/10.1109/ICME.2015.7177430
  • G. K. Wallace, "The JPEG still picture compression standard," in IEEE Trans. on Consumer Electronics, vol. 38, no. 1, pp. xviii-xxxiv, Feb 1992. https://dx.doi.org/10.1109/30.125072
  • A. Skodras, C. Christopoulos and T. Ebrahimi, "The JPEG 2000 still image compression standard," in IEEE Signal Process. Mag., vol. 18, no. 5, pp. 36-58, Sep 2001. https://dx.doi.org/10.1109/30.125072
  • M. Rabbani and R. Joshi, "An overview of the JPEG2000 still image compression standard (2002)," Signal Process.: Image Commun., vol. 17, no. 1, pp.3-48, 2002. https://dx.doi.org/10.1007/978-0-387-78414-4_99
  • S. Ahadpour and Y. Sadra, "Chaotic trigonometric Haar wavelet with focus on image encryption," J. of Discrete Math. Sciences and Cryptography, vol. 20, no. 5, pp. 1217-1239. https://dx.doi.org/10.1080/09720529.2016.1187958, 2017.
  • S. Ahadpour, Y. Sadra and M. Sadeghi, "Image encryption based on gradient Haar wavelet and rational order chaotic maps," Annals. Comput. Sci. Series, vol. 14, no. 1, pp. 59-66, 2016.
  • N. Ahmed, T. Natarajan and K. R. Rao, "Discrete cosine transform," IEEE Trans. on Comput., vol. C-23, no. 1, pp. 90-93, Jan. 1974. https://dx.doi.org/10.1109/T-C.1974.223784
  • J. L. Mitchell and W. B. Pennebaker, "JPEG: Still image data compression standard," Springer (3rd Ed.), 1993.
  • C. Christopoulos, A. Skodras and T. Ebrahimi, "The JPEG2000 still image coding system: An overview," in IEEE Trans. on Consumer Electronics, vol. 46, no. 4, pp. 1103-1127, 2000. https://dx.doi.org/10.1109/30.920468
  • D. Taubman, "High performance scalable image compression with EBCOT," in IEEE Trans. on Image Process., vol. 9, no. 7, pp. 1158-1170, 2000. Doi: https://dx.doi.org/10.1109/83.847830
  • S. G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," in IEEE Trans. on Pattern Analysis and Mach. Intell., vol. 11, no. 7, pp. 674-693, 1989.
  • R. C. Gonzalez and R. E. Woods, Digital image processing, in John Wiley & Sons (2nd Ed.), Prentice Hall, New Jersey.
  • T. Acharya and A. K. Ray, Image process. principles and appl., New Jersey: John Wiley & Sons, 2005.
  • A. Boggess and F. J. Narcowich, A first course in wavelets with Fourier analysis, John Wiley & Sons, (2nd Ed.), 2009.
  • Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multiscale structural similarity for image quality assessment," The Thrity-Seventh Asilomar Conf. on Signals, Systems & Comput., vol. 2, pp. 1398-1402, 2003.
  • Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. on Image Process., vol. 13, no. 4, pp. 1-14, 2004.
  • R. J. Hyndman and A. B. Koehler, "Another look at measures of forecast accuracy," Int. J. of Forecasting, vol. 22, no. 4, pp. 679-688, 2006. https://dx.doi.org/10.1016/j.ijforecast.2006.03.001
  • Q. Huynh-Thu and M. Ghanbari, "Scope of validity of PSNR in image/video quality assessment," Electronics Letters, vol. 44, no. 13, pp. 800-801, 2008. https://dx.doi.org/10.1049/el:20080522

Abstract Views: 312

PDF Views: 0




  • A New Image Compression by Gradient Haar Wavelet

Abstract Views: 312  |  PDF Views: 0

Authors

Yaser Sadra
Department of Computer Sciences, Shandiz Institute of Higher Education, Mashhad, Iran, Islamic Republic of

Abstract


With the development of human communications, the usage of visual communications has also increased. The advancement of image compression methods is one of the main reasons for the enhancement. This paper first presents main modes of image compression methods such as JPEG and JPEG2000 without mathematical details. Also, the paper describes gradient Haar wavelet transforms in order to construct a priliminary image compression algorithm so that sub images inherit the same amount of original image information. Then, a new image compression method is proposed based on the preliminary image compression algorithm that can improve standards of image compression. The new method is compared with original modes of JPEG and JPEG2000 (based on Haar wavelet) by image quality measures such as MAE, PSNAR, and SSIM. The image quality and statistical results confirm that can boost image compression standards. It is suggested that the new method is used in a part or all of an image compression standard.

Keywords


Digital Images, Image Communication, Image Decomposition, Image Storage, Image Quality, Wavelet.

References





DOI: https://doi.org/10.17010/ijcs%2F2020%2Fv5%2Fi2-3%2F152207