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Micro and Nanosystems

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ISSN (Print): 1876-4029
ISSN (Online): 1876-4037

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Research Article

Design and Development of a Hardware Efficient Image Compression Improvement Framework

Author(s): Hasanujjaman*, Arnab Banerjee, Utpal Biswas and Mrinal K. Naskar

Volume 12, Issue 3, 2020

Page: [217 - 225] Pages: 9

DOI: 10.2174/1876402912666200128125733

Abstract

Background: In the region of image processing, a varied number of methods have already initiated the concept of data sciences optimization, in which, numerous global researchers have put their efforts upon the reduction of compression ratio and increment of PSNR. Additionally, the efforts have also separated into hardware and processing sections, that would help in emerging more prospective outcomes from the research. In this particular paper, a mystical concept for the image segmentation has been developed that helps in splitting the image into two different halves’, which is further termed as the atomic image. In-depth, the separations were done on the bases of even and odd pixels present within the size of the original image in the spatial domain. Furthermore, by splitting the original image into an atomic image will reflect an efficient result in experimental data. Additionally, the time for compression and decompression of the original image with both Quadtree and Huffman is also processed to receive the higher results observed in the result section. The superiority of the proposed schemes is further described upon the comparison basis of performances through the conventional Quadtree decomposition process.

Objective: The objective of this present work is to find out the minimum resources required to reconstruct the image after compression.

Method: The popular method of quadtree decomposition with Huffman encoding used for image compression.

Results: The proposed algorithm was implemented on six types of images and got maximum PSNR of 30.12dB for Lena Image and a maximum compression ratio of 25.96 for MRI image.

Conclusion: Different types of images are tested and a high compression ratio with acceptable PSNR was obtained.

Keywords: Atomic image, Compression Ratio (CR), PSNR, Time for Compression (TC), Time for Decompression (TD), quadtree.

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Graphical Abstract

[1]
Jain, A.K. Image data compression: A review. Proc. IEEE, 1981, 69(3), 349-389.
[http://dx.doi.org/10.1109/PROC.1981.11971]
[2]
Jain, A.K. Advances in mathematical models for image processing. Proc. IEEE, 1981, 69(5), 502-528.
[http://dx.doi.org/10.1109/PROC.1981.12021]
[3]
Pratt, W.K. Image Transmission Techniques; Academic: New York, 1979.
[4]
Ahmed, N.; Natarjan, T.; Rao, K.R. Discrete cosine transform. IEEE Trans. Comput., 1974, C-23, 90-93.
[http://dx.doi.org/10.1109/T-C.1974.223784]
[5]
Jain, A.K. A sinusoidal family of unitary transforms. IEEE Trans. Pattern Anal. Mach. Intell., 1979, 1(4), 356-365.
[http://dx.doi.org/10.1109/TPAMI.1979.4766944] [PMID: 21868870]
[6]
Vetterli, M. Multidimensional subband coding: Some theory and algorithms. Signal Processing, 1984, 6, 97-112.
[http://dx.doi.org/10.1016/0165-1684(84)90012-4]
[7]
Woods, J.W.; O’Neil, S.D. Subband coding of images. IEEE Trans. Acoust. Speech Signal Process., 1986, 34(5), 1278-1288.
[http://dx.doi.org/10.1109/TASSP.1986.1164962]
[8]
Gray, R.M. Vector quantization. IEEE ASSP Mag., 1984, 1(2), 4-29.
[http://dx.doi.org/10.1109/MASSP.1984.1162229]
[9]
Baker, R.L. Vector quantization of digital images, Ph.D. dissertation, Stanford University: Stanford, CA. 1985.
[10]
Gersho, A.; Ramamurthi, B. Image coding using vector quantization. Proc. Int. Conf ASSP, 1982, 7, 428-431.
[http://dx.doi.org/10.1109/ICASSP.1982.1171673]
[11]
Vaisey, D.J.; Gersho, A. Variable block-size image coding. Proc. IEEE Int. Conf. Acoust. Speech Signal Processing (ICASSP), 1987, 12, 1051-1054.
[http://dx.doi.org/10.1109/ICASSP.1987.1169563]
[12]
Vaisey, J.; Gersho, A. Image compression with variable block size segmentation. IEEE Trans. Signal Process., 1992, 40, 2040-2060.
[http://dx.doi.org/10.1109/78.150005]
[13]
Chiu, C-Y.; Baker, R.L. Quad-tree product vector quantization of images. Proc. SPIE Conf. Advances Image Compression Automat. Target Recogn, 1989, 1099, 142-153.
[http://dx.doi.org/10.1117/12.960463]
[14]
Strobach, P. Tree-structured scene adaptive coder. IEEE Trans. Commun., 1990, 38(4), 477-486.
[http://dx.doi.org/10.1109/26.52659]
[15]
Strobach, P. Quadtree-structured recursive plane decomposition coding of images. IEEE Trans. Signal Process., 1991, 39, 1380-1397.
[http://dx.doi.org/10.1109/78.136544]
[16]
Nasrabadi, N.M.; Lin, S.E.; Feng, Y. Interframe hierarchical vector quantization. Opt. Eng., 1989, 28, 717-725.
[http://dx.doi.org/10.1117/12.7977028]
[17]
Tamminen, M.; Samet, H. Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Trans. Pattern Anal. Mach. Intell., 1988, 10, 579-586.
[http://dx.doi.org/10.1109/34.3918]
[18]
Hunter, G.M.; Steiglitz, K. Operations on images using quad trees. IEEE Trans. Pattern Anal. Mach. Intell., 1979, 1(2), 145-153.
[http://dx.doi.org/10.1109/TPAMI.1979.4766900] [PMID: 21868843]
[19]
Sudarsana, P.; Manohar, M.; Iyengar, S.S. Template quadtrees for representing region and line data present in binary images. Comput. Vis. Graph. Image Process., 1990, 51, 338-354.
[http://dx.doi.org/10.1016/0734-189X(90)90007-I]
[20]
Samet, H.; Webber, R.E. On encoding boundaries with quadtrees. IEEE Trans. Pattern Anal. Mach. Intell., 1984, 6(3), 365-369.
[http://dx.doi.org/10.1109/TPAMI.1984.4767529] [PMID: 21869203]
[21]
Wilson, R. Quad-tree predictive coding: A new class of image data compression algorithms Proc. Int. Conf. Acoustics, Speech. Signal Processing, 1984, 9, 527-530.
[http://dx.doi.org/10.1109/ICASSP.1984.1172463]
[22]
Cohen, Y.; Landy, M.S.; Pavel, M. Hierarchical coding of binary images. IEEE Trans. Pattern Anal. Mach. Intell., 1985, 7(3), 284-298.
[http://dx.doi.org/10.1109/TPAMI.1985.4767657] [PMID: 21869263]
[23]
Fuhrmann, D. Quadtree traversal algorithms for pointer-based and depth first representations. IEEE Trans. Pattern Anal. Mach. Intell., 1988, 10, 955-960.
[http://dx.doi.org/10.1109/34.9118]
[24]
Gargantini, I. An effective way to represent quadtrees. Commun. ACM, 1982, 25, 905-910.
[http://dx.doi.org/10.1145/358728.358741]
[25]
Grosky, W.I.; Jain, R. Optimal quadtrees for image segments. IEEE Trans. Pattern Anal. Mach. Intell., 1983, 5(1), 77-83.
[http://dx.doi.org/10.1109/TPAMI.1983.4767348] [PMID: 21869087]
[26]
Holroyd, F.C.; Mason, D.C. Efficient linear quadtree construction algorithm. Image Vis. Comput., 1990, 8(3), 218-224.
[http://dx.doi.org/10.1016/0262-8856(90)90068-G]
[27]
Jones, L.P.; Iyengar, S.S. Space and time efficient virtual quadtress. IEEE Trans. Pattern Anal. Mach. Intell., 1984, 6(2), 244-247.
[http://dx.doi.org/10.1109/TPAMI.1984.4767508] [PMID: 21869188]
[28]
Samet, H. An algorithm for converting rasters to quadtrees. IEEE Trans. Pattern Anal. Mach. Intell., 1981, 3(1), 93-95.
[http://dx.doi.org/10.1109/TPAMI.1981.4767054] [PMID: 21868922]
[29]
Shaffer, C.A.; Samet, H. Optimal quadtree construction algorithms. Comput. Vis. Graph. Image Process., 1987, 37, 402-419.
[http://dx.doi.org/10.1016/0734-189X(87)90045-4]

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