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Image Compression Based on a Hybrid Wavelet Packet and Directional Transform (HW&DT) Method

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Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

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Abstract

In this paper, a Hybrid Wavelet Packet and Directional transform (HW&DT) method is proposed to compress an image effectively. The image is pixel de-correlated using Daubechies Wavelet Packet transform and set of wavelet packet coefficients are transformed using Directional transform. The Directional transform pairs DCT-II, DCT-V, DCT-VIII and DST-VII are useful in retrieving the texture information in different directions. Then the coefficients of the hybrid transform are uniformly quantized and entropy coded using Huffman coding, generating the Bit-Stream. The performance evaluation of the proposed work is done using the compression parameters like Structural Similarity Index (SSIM), Bit-Saving (%), Peak Signal to Noise ratio (PSNR) and Mean Square Error (MSE). The experimental results confirm the improvement in Bit-Saving for the image.

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References

  1. Statista. http://www.statista.com/topics/2018/whatsapp/. Last accessed 02 Dec 2019

  2. Zephoria Digital marketing. http://www.zephoria.com/top-15-valuable-facebook-statistics/. Last accessed 02 Dec 2019

  3. Strutz T (2015) Context-based predictor blending for lossless color image compression. IEEE Trans Circuits Syst Video Technol 26(4):687–695

    Article  Google Scholar 

  4. Leung R, Taubman D (2005) Transform and embedded coding techniques for maximum efficiency and random accessibility in 3-D scalable compression. IEEE Trans Image Process 14(10):1632–1646

    Article  Google Scholar 

  5. Song HS, Cho NI (2009) DCT-based embedded image compression with a new coefficient sorting method. IEEE Signal Process Lett 16(5):410–413

    Article  Google Scholar 

  6. Ponomarenko NN, Egiazarian KO, Lukin VV, Astola JT (2007) High-quality DCT-based image compression using partition schemes. IEEE Signal Process Lett 14(2):105–108

    Article  Google Scholar 

  7. Wallace GK (1992) The JPEG still picture compression standard. IEEE Trans Consum Electron 38(1):xviii–xxiv

    Article  Google Scholar 

  8. Ichigaya A, Nishida Y, Nakasu E (2008) Nonreference method for estimating PSNR of MPEG-2 coded video by using DCT coefficients and picture energy. IEEE Trans Circuits Syst Video Technol 18(6):817–826

    Article  Google Scholar 

  9. Ngan KN, Chai D, Millin A (1996) Very low bit rate video coding using H. 263 coder. IEEE Trans Circuits Syst Video Technol 6(3):308–312

    Article  Google Scholar 

  10. Kalva H (2006) The H. 264 video coding standard. IEEE Multimedia 13(4):86–90

    Article  Google Scholar 

  11. Sullivan GJ, Ohm JR, Han WJ, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668

    Article  Google Scholar 

  12. Zhou M, Gao W, Jiang M, Yu H (2012) HEVC lossless coding and improvements. IEEE Trans Circuits Syst Video Technol 22(12):1839–1843

    Article  Google Scholar 

  13. Shi C, Zhang J, Zhang Y (2015) A novel vision-based adaptive scanning for the compression of remote sensing images. IEEE Trans Geosci Remote Sens 54(3):1336–1348

    Article  Google Scholar 

  14. Christopoulos C, Skodras A, Ebrahimi T (2000) The JPEG2000 still image coding system: an overview. IEEE Trans Consum Electron 46(4):1103–1127

    Article  Google Scholar 

  15. Thakur VS, Gupta S, Thakur K (2017) Hybrid WPT-BDCT transform for high-quality image compression. IET Image Proc 11(10):899–909

    Article  Google Scholar 

  16. Phanprasit T (2013) Compression of medical image using vector quantization. In: The 6th 2013 biomedical engineering international conference. IEEE, Amphur Muang, Thailand, pp 1–4

    Google Scholar 

  17. Hernández-Cabronero M, Blanes I, Pinho AJ, Marcellin MW, Serra-Sagristà J (2016) Progressive lossy-to-lossless compression of DNA microarray images. IEEE Signal Process Lett 23(5):698–702

    Article  Google Scholar 

  18. Zeng B, Fu J (2008) Directional discrete cosine transforms—a new framework for image coding. IEEE Trans Circuits Syst Video Technol 18(3):305–313

    Article  Google Scholar 

  19. Zhao X, Zhang L, Ma S, Gao W (2010) Rate-distortion optimized transform for intra-frame coding. In: 2010 IEEE international conference on acoustics, speech and signal processing. IEEE, Dallas, pp 1414–1417

    Google Scholar 

  20. Saxena A, Fernandes FC (2012) On secondary transforms for intra prediction residual. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, Kyoto, pp 1201–1204

    Google Scholar 

  21. Zhao X, Chen J, Karczewicz M, Said A, Seregin V (2018) Joint separable and non-separable transforms for next-generation video coding. IEEE Trans Image Process 27(5):2514–2525

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Vellore Institute of Technology, Chennai Campus, Chennai, India

    P. Madhavee Latha & A. Annis Fathima

Authors
  1. P. Madhavee Latha
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  2. A. Annis Fathima
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Corresponding author

Correspondence to A. Annis Fathima .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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Madhavee Latha, P., Annis Fathima, A. (2021). Image Compression Based on a Hybrid Wavelet Packet and Directional Transform (HW&DT) Method. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_33

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