General form for obtaining discrete orthogonal moments

Zhu, Hongqing; Liu, M.; Hu, Shu; Zhang, Hengliang; Luo, Limin

doi:10.1049/iet-ipr.2009.0195

Cited by 121 publications

(88 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of iterations required for the algorithm is set as 10. In order to compare the performance of the proposed evolutionary selection method (ESM) on the compression application, the sequential selection method (SSM) [19] and random selection method (RSM) [24] have been considered. MATLAB codes were developed for the proposed algorithm and executed.…”

Section: Resultsmentioning

confidence: 99%

“…A general form for Krawtchouk, Charlier, Tchebichef, Mexiner, and Hahn moments was proposed by Zhu et al recently [19]. Of the three moments, namely Tchebichef, Krawtchouk, and Hahn, considered in for their study for the task of compression, the Tchebichef outperformed others by reaching the highest value of peak-signal-to-noise ratio (PSNR), and the Hahn performed second best.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary optimized discrete Tchebichef moments for image compression applications

Hameed¹,

Duraisamy²,

Ameerbasha³

et al. 2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Evolutionary optimized coefficients of discrete orthogonal Tchebichef moment transform (TMT) are utilized in this study to ameliorate the quality of the traditional moments-based image compression methods. Most of the existing methods compute moment-transform coefficients for the input image and then select the coefficients sequentially downward to a certain order based on the desired compression ratio. However, the proposed method divides the input image into nonoverlapping square blocks of specific size in order to circumvent the problem of numerical instability and then computes the TMT coefficients for each block. In this work, a real-coded genetic algorithm is employed to optimize the TMT coefficients of each block, which produces reconstructed images of better quality for the desired compression ratio. Here the optimization is carried out by minimizing the mean square error function. Standard test images of two different sizes (128 × 128 and 256 × 256) have been subjected to the proposed compression method for the block sizes (4 × 4 and 8 × 8) in order to assess its performance. The results reveal that the proposed real-coded genetic algorithm-based method outperforms others, namely the conventional sequential selection method and simple random optimization method, for the chosen input images in terms of the task of compression.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary optimized discrete Tchebichef moments for image compression applications

Hameed¹,

Duraisamy²,

Ameerbasha³

et al. 2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…or, equivalently, a recurrence relation with respect to x can be found in the work of Zhu et al (2010). (f) show that the difference between the parameters α and β will make the distribution of polynomials tend to one side of the definition of the domain.…”

Section: Bivariate Hahn Polynomials and Momentsmentioning

confidence: 99%

Bivariate Hahn moments for image reconstruction

Wu¹,

Yan

2014

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

This paper presents a new set of bivariate discrete orthogonal moments which are based on bivariate Hahn polynomials with non-separable basis. The polynomials are scaled to ensure numerical stability. Their computational aspects are discussed in detail. The principle of parameter selection is established by analyzing several plots of polynomials with different kinds of parameters. Appropriate parameters of binary images and a grayscale image are obtained through experimental results. The performance of the proposed moments in describing images is investigated through several image reconstruction experiments, including noisy and noise-free conditions. Comparisons with existing discrete orthogonal moments are also presented. The experimental results show that the proposed moments outperform slightly separable Hahn moments for higher orders.

show abstract

“…A general way to obtain normalized discrete orthogonal polynomials ( ) n p x  is by the following recurrence relation (H. Zhu et al [12]). …”

Section: Discrete Orthogonal Polynomialsmentioning

confidence: 99%

Color Image Reconstruction by Discrete Orthogonal Moment

Rivera-López¹,

Camacho-Bello²

2017

JDAIP

View full text Add to dashboard Cite

Image reconstruction can help to determine how well an image may be characterized by a small finite set of its moments. Also, we can identify the number of descriptors needed to describe an image. In this work, we present a comparative analysis using different set of discrete orthogonal moments in terms of normalized image reconstruction error (NIRE). Color image reconstruction is performed with different color channels and various orders of different discrete orthogonal moments. Finally the results obtained by the reconstruction of three color images with different families of orthogonal moments and an error analysis to compare their capacity of description are presented, also the conclusions obtained from this work are presented.

show abstract

General form for obtaining discrete orthogonal moments

Cited by 121 publications

References 13 publications

Evolutionary optimized discrete Tchebichef moments for image compression applications

Evolutionary optimized discrete Tchebichef moments for image compression applications

Bivariate Hahn moments for image reconstruction

Color Image Reconstruction by Discrete Orthogonal Moment

Contact Info

Product

Resources

About