Abstract
The objective of this paper is to provide a proposed method for radiographic image compression with maximum Compression Ratio (CR) as possible and keeping all details, especially in the Region Of Interest (ROI) that contains the important information in the image. In the proposed method; firstly, the ROI is separated from the image background using an automatic threshold based on the occurrence histogram of the variance image, then the image background is compressed with the maximum possible compression ratio using image pyramid compression followed by lossy Vector Quantization (VQ) compression technique based on Generalized Lloyd Algorithm (GLA) method for generating the codebook. After that, ROI is compressed using the Huffman Code (HC) with low compression ratio and with minimum loss in details. Finally, the compressed image is obtained by combing both the compressed background and the compressed ROI. The results are evaluated by calculating the Normalized Cross Correlation (NCC) and the Structural Similarity Index (SSIM) between the original image and the recovered image after decompression at different compression ratios. The obtained results are compared with the results obtained from compressing the whole image without separation using lossy VQ, HC, Discrete Cosine Transform (DCT), and JPEG2000 compression methods. The results show that, the proposed method owns more reliable performance than completely compressing the radiographic image without separation using VQ compression or Huffman coding compression.
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References
Almurib HAF, Kumar N, Lombardi F (2017) Approximate DCT image compression using inexact computing. IEEE Trans Comput 67(2):149–159. https://doi.org/10.1109/TC.2017.2731770
Anitha S (2015) Lossless image compression and decompression using Huffman coding. Int Res J Eng Technol 2(1)
Bruylants T, Munteanu A, Schelkens P (2015) Wavelet based volumetric medical image compression. Sig Process Image Commun 31:112–133. https://doi.org/10.1016/j.image.2014.12.007
Chiranjeevi K, Jena UR (2017) Image compression based on vector quantization using cuckoo search optimization technique. Ain Shams Engineering Journal, In Press, Corrected Proof. https://doi.org/10.1016/j.asej.2016.09.009
Dubey VG, Singh J (2012) 3D medical image compression using Huffman encoding technique. Int J Sci Res Publ 2(9). http://www.ijsrp.org/research-paper-0912.php?rp=P09217
Ernawan F, Kabir N, Zamli KZ (2017) An efficient image compression technique using Tchebichef bit allocation. Optik Int J Light Electron Optics 148:106–119. https://doi.org/10.1016/j.ijleo.2017.08.007
Haweel RT, El–Kilani WS, Ramadan HH (2016) Fast approximate DCT with GPU implementation for image compression. J Vis Commun Image Represent 40(A):357–365. https://doi.org/10.1016/j.jvcir.2016.07.003
Hosseini SM, Nilchi ARN (2012) Medical ultrasound image compression using contextual vector quantization. Comput Biol Med 42(7):743–750. https://doi.org/10.1016/j.compbiomed.2012.04.006
Hsu WY (2012) Improved watershed transform for tumour segmentation: Application to mammogram image compression. Expert Syst Appl 39(4):3950–3955. https://doi.org/10.1016/j.eswa.2011.08.148
Hu YC, Chang CC (2003) An effective codebook search algorithm for vector quantization. Imaging Sci J 51(4):221–234. https://doi.org/10.1080/13682199.2003.11784428
Hu YC, Su BH, Chiang TC (2008) Fast VQ codebook search for greyscale image coding. Image Vision Comput 26(5):657–666. https://doi.org/10.1016/j.imavis.2007.08.001
Huang CC, Tsai DS, Horng G (2009) A fast VQ codebook generation algorithm based on OTSU histogram threshold. Fundamenta Informaticae 91:563–579
Jiang H, Ma Z, Hu Y, Yang B, Zhang L (2012) Medical Image compression based on vector quantization with variable block sizes in wavelet domain. Comput Intell Neurosci. 2012. https://doi.org/10.1155/2012/541890
Jiang W, Liu P, Wen F (2017) An improved vector quantization method using deep neural network. AEU Int J Electron Commun 72:178–183. https://doi.org/10.1016/j.aeue.2016.12.002
Karimi N, Samavi S, Soroushmeh SMR, Shirani S, Najarian K (2016) Toward practical guideline for design of image compression algorithms for biomedical applications. Expert Syst Appl 56:360–367. https://doi.org/10.1016/j.eswa.2016.02.047
Karri C, Jena U (2016) Fast vector quantization using a Bat algorithm for image compression. Eng Sci Technol Int J 19(2):769–778. https://doi.org/10.1016/j.jestch.2015.11.003
Kasban H (2017) A spiral based image watermarking scheme using Karhunen–Loeve and discrete Hartley transform. Multidimensional Syst Signal Process Springer 28(2):573–595. https://doi.org/10.1007/s11045-015-0361-4
Kasban H, Zahran O, Arafa H, El–Kordy M, Elaraby SMS, AbdEl–Samie FE (2011) Welding defect detection from radiography images with a cepstral approach. NDT&E Int 44:226–231. https://doi.org/10.1016/j.ndteint.2010.10.005
Kasmeera KS, James SP, Sreekumar K (2016) Efficient compression of secured images using subservient data and Huffman coding. Procedia Technol 25:60–67. https://doi.org/10.1016/j.protcy.2016.08.081
Kaur D, Kaur K (2013) Huffman based LZW lossless image compression using retinex algorithm. Int J Adv Res Comput Commun Eng 2(8):3145–3151
Kiruba M, Sumathy V (2018) RPF–DTT: register pre-allocation based folded discrete tchebichef transform (DTT) architecture for image compression. Integr VLSI J 60:13–24. https://doi.org/10.1016/j.vlsi.2017.07.003
Kumar M, Vaish A (2017) An efficient encryption-then-compression technique for encrypted images using SVD. Digit Signal Proc 60:81–89. https://doi.org/10.1016/j.dsp.2016.08.011
Linde Y, Andres B, Gray RB (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1):84–95. https://doi.org/10.1109/TCOM.1980.1094577
Messaoudi A, Srairi K (2016) Colour image compression algorithm based on the DCT transform using difference lookup table. IEEE Electron Lett 52(20):1685 – 1686. https://doi.org/10.1049/el.2016.2115
Nguyen BP, KongChui C, HengOng S, Chang S (2011) An efficient compression scheme for 4–D medical images using hierarchical vector quantization and motion compensation. Comput Biol Med 41(9):843–856. https://doi.org/10.1016/j.compbiomed.2011.07.003
Pan JS, Lu ZM, Sun SH (2003) An efficient encoding algorithm for vector quantization based on subvector technique. IEEE Trans Image Process 12(3):265–270. https://doi.org/10.1109/TIP.2003.810587
Pan Z, Kotani K, Ohmi T (2005) Fast encoding method for vector quantization using modified L2–norm pyramid. EEE Signal Processing Lett 12(9):609–612. https://doi.org/10.1109/LSP.2005.851263
Ra SW, Kim JK (1993) A fast mean–distance–ordered partial codebook search algorithm for image vector quantization. IEEE Trans on Circuits Syst 40(9):576–579. https://doi.org/10.1109/82.257335
Sunder RS, Eswaran C, Sriraam N (2006) Medical image compression using 3–D Hartley transform. Comput Biol Med 36(9):958–973. https://doi.org/10.1016/j.compbiomed.2005.04.005
Vaish A, Gautam S, Kumar M (2017) A wavelet based approach for simultaneous compression and encryption of fused images. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2017.01.005
Venugopal D, Mohan S, Raja S (2016) An efficient block based lossless compression of medical images. Optik 127:754–758. https://doi.org/10.1016/j.ijleo.2015.10.154
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):1–14. https://doi.org/10.1109/TIP.2003.819861
Wen J, Ma C, Zhao J (2014) FIVQ algorithm for interference hyper–spectral image compression. Optics Commun 322:97–104. https://doi.org/10.1016/j.optcom.2014.02.016
Wu KS, Lin JC (2000) Fast VQ encoding by an efficient kick–out condition. IEEE Trans Circuits Syst 10(1):59–62. https://doi.org/10.1109/76.825859
Xiao B, Lu G, Zhang Y, Li W, Wang G (2016) Lossless image compression based on integer discrete Tchebichef transform. Neurocomputing 214:587–593. https://doi.org/10.1016/j.neucom.2016.06.050
Zahran O, Kasban H, El–Kordy M, AbdEl–Samie FE (2013) Automatic weld defect identification from radiographic images. NDT&E Int 57:26–35. https://doi.org/10.1016/j.ndteint.2012.11.005
Zhang M, Tong X (2017) Joint image encryption and compression scheme based on IWT and SPIHT. Opt Lasers Eng 90:254–274. https://doi.org/10.1016/j.optlaseng.2016.10.025
Zhang GM, Olofsson T, Stepinski T (2004) Ultrasonic NDE image compression by transform and subband coding. NDT&E Int 37(4):325–333. https://doi.org/10.1016/j.ndteint.2003.10.007
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Kasban, H., Hashima, S. Adaptive Radiographic Image Compression Technique using Hierarchical Vector Quantization and Huffman Encoding. J Ambient Intell Human Comput 10, 2855–2867 (2019). https://doi.org/10.1007/s12652-018-1016-8
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DOI: https://doi.org/10.1007/s12652-018-1016-8