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Using tchebichef moment for fast and efficient image compression

Authors:

Hidayah Rahmalan,

Siaw Lang WongAuthors Info & Claims

Pattern Recognition and Image Analysis, Volume 20, Issue 4

Pages 505 - 512

https://doi.org/10.1134/S1054661810040115

Published: 01 December 2010 Publication History

Abstract

Orthogonal moment is known as better moment functions compared to the non-orthogonal moment. Among all the orthogonal moments, Tchebichef Moment appear to be the most recent moment functions that still attract the interest among the computer vision researchers. This paper proposes a novel approach based on discrete orthogonal Tchebichef Moment for an efficient image compression. The image compression is useful in many applications especially related to images that are needed to be seen in small devices such as in mobile phone. Meanwhile, the method incorporates simplified mathematical framework techniques using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. In addition, a comparison between Tchebichef Moment compression and JPEG compression is conducted. The result shows significant advantages for Tchebichef Moment in terms of its image quality and compression rate. Tchebichef moment provides a more compact support to the image via sub-block reconstruction for compression. Tchebichef Moment Compression is able to perform potentially better for a broader domain on real digital images and graphically generated images.

References

[1]

A. K. Jain, Fundamentals of Digital Image Processing (Prentice Hall, 2006), p. 172.

[2]

C. A. Christopoulos, W. Philips, A. N. Skodras, and J. Cornells, "Discrete Cosine Transform Coding of Images," in Proc. Int. Conf. DSP and CAES, Cyprus, Nicosia, 1993, vol. 1, pp. 164-169.

[3]

G. K. Wallace, "The JPEG Still Picture Compression Standard," Commun. ACM 34 (4), 31-44 (1991).

Digital Library

[4]

H. Rahmalan, "Detecting Crowd Movement Using Moment Invariants," in Int. Conf. on Image Processing, Computer Vision and Pattern Recognition IPCV09, Nevada, Las Vegas, 2009.

[5]

H. Rahmalan, M. S. Nixon, and J. N. Carter, "On Crowd Density Estimation for Surveillance," in Proc. Int. Conf. on Crime Detection and Prevention, London, 2006.

[6]

H. Rahmalan, Nanna Suryana, and Nur Azman Abu, "A General Approach for Measuring Crowd Movement," in Malaysian Technical Universities Conf. and Exhibition on Engineering and Technology MUCEET2009 (Pahang, Kuantan, 2009), pp. 98-103.

[7]

H. Zhu, H. Shu, T. Xia, L. Luo, and J. L. Coatrieux, "Translation and Scale Invariants of Tchebichef Moments," Pattern Rec. 40(9), 2530-2542 (2007); ISSN 0031_3203.

Digital Library

[8]

J. D. Shutler, PhD Thesis (University of Southampton, Department Electronic and Computer Science, 2001).

[9]

K. Nakagaki and R. Mukundan, "A Fast 4 × 4 Forward Discrete Tchebichef Transform Algorithm," IEEE Signal Proc. Lett. 14(10), 684-687 (2007).

[10]

L. Wang and G. Healey, "Using Zernike Moments for the Illumination and Geometry Invariant Classification of Multispectral Texture," IEEE Trans. Image Proc. 7, 196-203 (1998).

Digital Library

[11]

L. Zhang, G. B. Qian, W. W. Xiao, and Z. Ji, "Geometric Invariant Blind Image Watermarking by Invariant Tchebichef Moments," Opt. Express J. 15, 2251-2261 (2007).

[12]

M. Rabbani and P. W. Jones, Digital Image Compression Techniques (SPIE Press Book); 1991, Vol. TT07.

[13]

M. Tuceryan, "Moment Based Texture Segmentation," Pattern Rec. Lett. 15, 659-668 (1994).

Digital Library

[14]

N. A. Abu, N. Suryana, and R. Mukundan, "Perfect Image Reconstruction Using Discrete Orthogonal Moments," in Proc. The 4th IASTED Int. Conf. Visualization, Imaging, and Image Processing VIIP2004, SPAIN, Marbella, 2004, pp. 903-907.

[15]

N. A. Abu, W. S. Lang, and S. Sahib, Image Super-Resolution via Discrete Tchebichef Moment, in Int. Conf. Computer Technology and Development, 2009, Vol. 2, pp. 315-319.

Digital Library

[16]

N. Ahmed, T. Neterajan, and K. R. Rao, "Discrete Cosine Transform," IEEE Trans. Comput. 23, 90-93 (1974).

Digital Library

[17]

R. Mukundan and K. R. Ramakrishnan, Moment Functions in Image Analysis--Theory and Applications (World Sci., 1998).

[18]

R. Mukundan and O. Hunt, "A Comparison of Discrete Orthogonal Basis Functions for Image Compression," in Proc. Conf. Image and Vision Computing New Zealand (IVCNZ), 2004, pp. 53-58.

[19]

R. Mukundan, "Some Computational Aspects of Discrete Orthonormal Moments," IEEE Trans. Image Proc. 13(8), 1055-1059 (2004).

Digital Library

[20]

R. Mukundan, "Improving Image Reconstruction Accuracy Using Discrete Orthonormal Moments," in Proc. Int. Conf. Imaging Systems, Science and Technology CISST2003, 2003, pp. 287-293.

[21]

R. Mukundan, S. H. Ong, and P. A. Lee, "Image Analysis by Tchebichef Moments," IEEE Trans. Image Process. 10(9), 1357-1364 (2001).

Digital Library

[22]

S. X. Liao and M. Pawlak, "On Image Analysis by Moments," IEEE Trans. Patt. Anal. Mach. Intell. 18 (3), 254-266 (1996).

Digital Library

[23]

T. Cho-Huak and R. T. Chin, "On Image Analysis by the Methods of Moments," IEEE Trans. Pattern Anal. Mach. Intellig. 10(4), 496-513 (1988); ISSN 0162- 8828.

Digital Library

[24]

T. Acharya and A. K. Ray, Image Processing: Principles and Applications (John Wiley, 2005), p. 365.

[25]

Wong Siaw Lang, Nur Azman Abu, and Hidayah Rahmalan, "Fast 4 × 4 Tchebichef Moment Image Compression," in Int. Conf. Soft Computing and Pattern Recognition (SoCPar2009), 2009, pp. 295-300.

Digital Library

Cited By

Chaker RBoua A(2024)Secure Color Image Encryption Using 9D Hyperchaotic System, Fibonacci Matrices of order m and Symplectic Quaternion-Fractional Hahn MomentsSN Computer Science10.1007/s42979-024-02862-w5:5Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02862-w
Yamni MDaoui AKarmouni HSayyouri MQjidaa HWang CJamil M(2023)A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony AlgorithmCircuits, Systems, and Signal Processing10.1007/s00034-023-02379-242:9(5602-5633)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1007/s00034-023-02379-2
Yamni MKarmouni HSayyouri MQjidaa H(2022)Quaternion cartesian fractional hahn moments for color image analysisMultimedia Tools and Applications10.1007/s11042-021-11432-881:1(737-758)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11042-021-11432-8
Show More Cited By

Using tchebichef moment for fast and efficient image compression
1. Computing methodologies
  1. Computer graphics
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis

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Abstract
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Information & Contributors

Information

Published In

cover image Pattern Recognition and Image Analysis

Pattern Recognition and Image Analysis Volume 20, Issue 4

December 2010

158 pages

ISSN:1054-6618

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Copyright © Copyright © 2010 Pleiades Publishing, Ltd.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2010

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Cited By

Chaker RBoua A(2024)Secure Color Image Encryption Using 9D Hyperchaotic System, Fibonacci Matrices of order m and Symplectic Quaternion-Fractional Hahn MomentsSN Computer Science10.1007/s42979-024-02862-w5:5Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02862-w
Yamni MDaoui AKarmouni HSayyouri MQjidaa HWang CJamil M(2023)A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony AlgorithmCircuits, Systems, and Signal Processing10.1007/s00034-023-02379-242:9(5602-5633)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1007/s00034-023-02379-2
Yamni MKarmouni HSayyouri MQjidaa H(2022)Quaternion cartesian fractional hahn moments for color image analysisMultimedia Tools and Applications10.1007/s11042-021-11432-881:1(737-758)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11042-021-11432-8
Hjouji ABouikhalene BEL-Mekkaoui JQjidaa H(2021)New set of adapted Gegenbauer–Chebyshev invariant moments for image recognition and classificationThe Journal of Supercomputing10.1007/s11227-020-03450-477:6(5637-5667)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11227-020-03450-4
Karmouni HYamni MEl Ogri ODaoui ASayyouri MQjidaa HTahiri AMaaroufi MAlami B(2021)Fast Computation of 3D Discrete Invariant Moments Based on 3D Cuboid for 3D Image ClassificationCircuits, Systems, and Signal Processing10.1007/s00034-020-01646-w40:8(3782-3812)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00034-020-01646-w
Jahid TKarmouni HSayyouri MHmimid AQjidaa H(2019)Fast Algorithm of 3D Discrete Image Orthogonal Moments Computation Based on 3D CuboidJournal of Mathematical Imaging and Vision10.1007/s10851-018-0860-761:4(534-554)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s10851-018-0860-7
Qayyum ASaeed Malik ASaad NIqbal MAbdullah MRasheed WAbdullah TBin Jafaar M(2019)Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approachNeural Computing and Applications10.1007/s00521-017-3300-531:8(3587-3607)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00521-017-3300-5
Karmouni HJahid TSayyouri MHmimid AQjidaa H(2019)Fast Reconstruction of 3D Images Using Charlier Discrete Orthogonal MomentsCircuits, Systems, and Signal Processing10.1007/s00034-019-01025-038:8(3715-3742)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00034-019-01025-0
Ernawan FKabir MZain J(2018)Bit allocation strategy based on Psychovisual threshold in image compressionMultimedia Tools and Applications10.1007/s11042-017-4999-977:11(13923-13946)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11042-017-4999-9
Paim GSantana GRocha LSoares LDa Costa EBampi S(2018)Exploring approximations in 4- and 8- point DTT hardware architectures for low-power image compressionAnalog Integrated Circuits and Signal Processing10.1007/s10470-018-1343-x97:3(503-514)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10470-018-1343-x

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