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Image compression and denoising using multiresolution region-based image description scheme

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Abstract

Lossy image compression removes redundant information which considerably reduces the size of files needed for storing images. Lossy compression at different resolutions can be used to remove redundant information at each resolution level. Wavelet transform is one of the most successful multiresolution tools that has been widely applied in image compression. However, wavelet transform suffers from high computational complexity. In this paper, we propose a novel multiresolution region-based image description scheme that can be used to transform any region-based image descriptor into a multiresolution structure. Our proposed multiresolution scheme uses the original image information independently from other preprocessing techniques such as filtering, region segmentation, or any prior assumptions that might result in an additional computational overhead. The tree structure of our multiresolution scheme is well suited for parallel processing, which further improves its computational efficiency. Based on this multiresolution scheme, we propose a novel image compression and denoising scheme that can compress and denoise images simultaneously. Our image compression and denoising scheme achieves multiresolution analysis and avoids blocking artifacts; it has high computational accuracy and efficiency as well as strong flexibility.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Nelson M, Gailly J (1995) The data compression book, 2nd edn. M.T. Books, Canada

    Google Scholar 

  2. Hussain A, Al-Fayadh A, Radi N (2018) Image compression techniques: a survey in lossless and lossy algorithms. Neurocomputing 300:44–69

    Article  Google Scholar 

  3. Chalasani R, Principe J, Ramakrishnan N (2013) A fast proximal method for convolutional sparse coding. In: 2013 International Joint Conference on Neural Networks (IJCNN), pp 1–5

  4. Wang Y, Zhang S, Liu H (2013) Study of image compression based on wavelet transform. In: Fourth International Conference on Intelligent Systems Design and Engineering Applications, pp 575–578

  5. Milani S, Zanuttigh P (2015) Compression of photo collections using geometrical information. In: IEEE International Conference on Multimedia & Expo (ICME), pp 1–6

  6. Liu S, Paul A, Zhang G, Jeon G (2015) A game theory-based block image compression method in encryption domain. J Supercomput 71:3353–3372

    Article  Google Scholar 

  7. Zhu F, Yan H (2022) An efficient parallel entropy coding method for JPEG compression based on GPU. J Supercomput 78:2681–2708

    Article  Google Scholar 

  8. Rane S, Boufounos P, Vetro A, Okada Y (2011) Low complexity efficient raw SAR data compression. Algorithms Syn Apert Radar Imag XVIII 8051:290–300

    Google Scholar 

  9. Li Q, Fu Y, Zhang Z, Fofanah A, Gao T (2022) Medical images lossless recovery based on POB number system and image compression. Multimed Tools Appl 81:11415–11440

    Article  Google Scholar 

  10. Liu X, An P, Chen Y, Huang X (2022) An improved lossless image compression algorithm based on Huffman coding. Multimed Tools Appl 81:4781–4795

    Article  Google Scholar 

  11. Chowdhury M, Khatun A (2012) Image compression using discrete wavelet transform. IJCSI Int J Comput Sci Issues 9:327–330

    Google Scholar 

  12. Uthayakumar J, Vengattaraman T, Arjunan S (2022) An efficient near lossless image compression algorithm using dissemination of spatial correlation for remote sensing color images. Wirel Person Commun 122:2963–2994

    Article  Google Scholar 

  13. Sharma S, Hada N, Choudhary G, Kashif S (2021) A lossless compression algorithm based on high frequency intensity removal for grayscale images. In: International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC, vol 1534, pp 818–831

  14. Singh A, Kirar K (2017) Review of image compression techniques. In: International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE), pp 172–174

  15. Cheng Z, Sun H, Takeuchi M, Katto J (2018) Deep convolutional auto encoder-based Lossy image compression. In: 2018 Picture Coding Symposium, pp 253–257

  16. Wang S, Wang S, Zhang X, Wang S, Ma S, Gao W (2019) Scalable facial image compression with deep feature reconstruction. In: IEEE International Conference on Image Processing (ICIP), pp 2691–2695

  17. Toderici G, Vincent D, Johnston N, Hwang S, Minnen D, Shor J, Covell M (2017) Full resolution image compression with recurrent neural networks. In: 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp 5306–5314

  18. Artuger F, Ozkaynak F (2018) Fractal image compression method for Lossy data compression. In: International Conference on Artificial Intelligence and Data Processing (IDAP), pp 1–6

  19. Lin Y, Liu F, Hernandez-Cabronero M, Ahanonu E, Marcellin M, Bilgin A, Ashok A (2019) Perception-optimized encoding for visually Lossy image compression. Proceedings of Data Compression Conference (DCC), p 592

  20. Huffman D (1952) A method for the construction of minimum-redundancy codes. Proc IRE 40:1098–1101

    Article  MATH  Google Scholar 

  21. Langdon G (1984) An introduction to arithmetic coding. IBM J Res Dev 28:135–149

    Article  MATH  Google Scholar 

  22. Welch T (1984) A technique for high-performance data-compression. Computer 17:8–19

    Article  Google Scholar 

  23. Lukin V, Zriakhov M, Ponomarenko N, Krivenko S, Miao Z (2010) Lossy compression of images without visible distortions and its application. In: IEEE 10th International Conference on Signal Processing Proceedings (ICSP2010), pp 698–701

  24. Chuah S, Dumitrescu S, Wu X (2013) l 2 optimized predictive image coding with l ∞ bound. IEEE Trans Image Process 22:5271–5281

    Article  Google Scholar 

  25. Woods J, Oneil S (1986) Subband coding of images. IEEE Trans Acoust Speech Signal Process 34:1278–1288

    Article  Google Scholar 

  26. Goyal V (2001) Theoretical foundations of transform coding. IEEE Signal Process Mag 18:9–21

    Article  Google Scholar 

  27. Xu M, Kuh A (1996) Image coding using feature map finite-state vector quantization. IEEE Signal Process Lett 3:215–217

    Article  Google Scholar 

  28. Gersho A, Gray R (1992) Vector quantization and signal compression, 1st edn. Springer, US

    Book  MATH  Google Scholar 

  29. Ahmed N, Natarajan T, Rao K (1974) Discrete cosine transform. IEEE Trans Comput 100:90–93

    Article  MATH  Google Scholar 

  30. Xiong Z, Ramchandran K, Orchard M, Zhang Y (1999) A comparative study of DCT- and wavelet-based image coding. IEEE Trans Circuits Syst Video Technol 9:692–695

    Article  Google Scholar 

  31. Gonzalez R, Woods R (2008) Digital image processing, 3rd edn. Pearson Prentice Hall, Hoboken

    Google Scholar 

  32. Goyal B, Dogra A, Agrawal S, Sohi B, Sharma A (2020) Image denoising review: from classical to state-of-the-art approaches. Inform Fus 55:220–244

    Article  Google Scholar 

  33. Yin S, Cao L, Ling Y, Jin G (2011) Image denoising with anisotropic bivariate shrinkage. Signal Process 91:2078–2090

    Article  MATH  Google Scholar 

  34. Rangarajan A, Chellappa R (1995). Markov random eld models in image processing, In: The Handbook of Brain Theory and Neural Networks, MA Arbib, pp 564–567.

  35. Sanches J, Nascimento J, Marques J (2008) Medical image noise reduction using the Sylvester-Lyapunov equation. IEEE Trans Image Process 17:1522–1539

    Article  MATH  Google Scholar 

  36. Shandoosti H, Rahemi Z (2019) Edge-preserving image denoising using a deep convolutional neural network. Signal Process 159:20–32

    Article  Google Scholar 

  37. Jifara W, Jiang F, Rho S, Cheng M, Liu S (2019) Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput 75:704–718

    Article  Google Scholar 

  38. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15:3736–3745

    Article  Google Scholar 

  39. Zhu X, Milanfar P (2010) Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans Image Process 19:3116–3132

    Article  MATH  Google Scholar 

  40. Yang X, Zhang Y, Li T, Guo Y, Zhou D (2021) Image super-resolution based on the down-sampling iterative module and deep CNN. Circuits Syst Signal Process 40:3437–3455

    Article  Google Scholar 

  41. Yang X, Li X, Li Z, Zhou D (2021) Image super -resolution based on deep neural network of multiple attention mechanism. J Vis Commun Image Represent 75:103019

    Article  Google Scholar 

  42. Yang X, Fan J, Wu C, Zhou D, Li T (2022) NasmamSR: a fast image super-resolution network based on neural architecture search and multiple attention mechanism. Multimed Syst 28:321–334

    Article  Google Scholar 

  43. Motwani M, Gadiya M, Motwani R, Harris F (2004) Survey of image denoising techniques. Proc GSPX 27:27–30

    Google Scholar 

  44. Zhao Y, Belkasim S (2016) Image compression and denoising algorithm based on multi-resolution discrete cosine transform. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), pp 110–116

  45. Ceylan M, Ucan O, Ozbay Y, Jennane R, Aufort G, Benhamou C (2010) Comparison of discrete wavelet transform and complex wavelet transform in hybrid skeletonization based on CVANN. J Istanbul Aydin Univ 2:27–51

    Google Scholar 

  46. Lindeberg T (1994) Scale-space theory: a basic tool for analyzing structures at different scales. J Appl Statist 21:225–270

    Article  Google Scholar 

  47. Jacquin A (1992) Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans Image Process 1:18–30

    Article  Google Scholar 

  48. Salembier P, Garrido L (2000) Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans Image Process 9:561–576

    Article  Google Scholar 

  49. Chakrabarti K, Ortega-Binderberger M, Porkaew K, Mehrotra S (2000) Similar shape retrieval in MARS. In: 2000 IEEE International Conference on Multimedia and Expo, vol 2, pp 709–712

  50. Wang X, Zhang J, Chang H (2004) Assessment of the performance of staring infrared imaging array based on microscanning modes. Int J Infra Millimeter Waves 25:905–916

    Article  Google Scholar 

  51. Fautz H, Honal M, Saueressig U, Schafer O, Kannengiesser S (2007) Artifact reduction in moving-table acquisitions using parallel imaging and multiple averages. Magn Reson Med 57:226–232

    Article  Google Scholar 

  52. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42:145–175

    Article  MATH  Google Scholar 

  53. Fei-Fei L, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: 2005 IEEE International Conference on Computer Vision and Pattern Recognition, vol 2, pp 524–531

  54. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE International Conference on Computer Vision and Pattern Recognition, vol 2, pp 2169–2178

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Authors and Affiliations

  1. College of Arts and Sciences, Troy University, Troy, AL, USA

    Yanjun Zhao

  2. College of Arts and Sciences, Georgia State University, Atlanta, GA, USA

    Saeid Belkasim

  3. College of Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Guillaume Aubry

Authors
  1. Yanjun Zhao
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  2. Saeid Belkasim
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  3. Guillaume Aubry
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Correspondence to Yanjun Zhao.

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Zhao, Y., Belkasim, S. & Aubry, G. Image compression and denoising using multiresolution region-based image description scheme. J Supercomput 79, 4243–4265 (2023). https://doi.org/10.1007/s11227-022-04806-8

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