Video Coding Technique Using Swarm Intelligence in 3-D Dual Tree Complex Wavelet Transform

Thamarai, M.; Shanmugalakshmi, R.

doi:10.1109/icmlc.2010.39

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…The dual-tree complex wavelet transform (DT-CWT) has been recently used in various signal and image processing applications [10], [11], [12] and [13]. It has desirable properties such as shift invariance, directional selectivity and lack of aliasing.…”

Section: Dual-tree Complex Wavelet Transformmentioning

confidence: 99%

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A multiplication-free framework for signal processing and applications in biomedical image analysis

Suhre

Keskin

Erşahin

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix. © 2013 IEEE

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Section: Dual-tree Complex Wavelet Transformmentioning

confidence: 99%

“…For each subwindow, a codifference matrix was computed using (2) and the feature vector from (13). The image signature is composed of s co-difference matrices of the same size.…”

Section: Classificationmentioning

confidence: 99%

A multiplication-free framework for signal processing and applications in biomedical image analysis

Suhre

Keskin

Erşahin

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The significant coefficients are selected using Multi objective optimization algorithm using PSO. In [12], the usage of 3-D dual tree wavelet transform with PSO in video coding with single objective function (Maximizing PSNR) is reported. The optimum subbands are selected using PSO algorithm to improve the PSNR value with the constraints of different bit rates.…”

Section: Introductionmentioning

confidence: 99%

Video Coding Technique with Multi Objective Particle Swarm Optimization and EZW

Thamarai¹,

Shanmugalakshmi²

2016

Journal of Electrical Engineering and Technology

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-Video coding plays an important role in video transmission and storage applications.Today's increasing order of multimedia applications led to a lot of research works in video coding in such a way that high compression ratio is achieved with the available bandwidth. Wavelet based image compression has witnessed great success in the past decade. Wavelet transform based motion compensated video codec performs better compression in order to meet the rate and distortion constraint in video transmission than the block based techniques. However, it is well known that the 2D DWT does not represent directional features of images efficiently. Lots of efforts have been put into multiscale directional representation. In this paper, video coding using directional transform DDWT is considered and its expansive nature is reduced by noise shaping algorithm. High compression ratio is achieved through the selection of optimal coefficients of DDWT using Multi Objective Particle Swarm Optimization (MOPSO) method. In this video coding technique, the objective functions of Entropy, Computation Time and Mean Square Error are considered for optimization with the constraints of bits per pixel and frame rate. The selected optimum coefficients are encoded using EZW method. The performance of the proposed method is compared with the standard 3D SPIHT coding.

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“…The dual-tree complex wavelet transform (DT-CWT) has been recently used in various signal and image processing applications [2], [3], [4], [5]. It has desirable properties such as shift invariance, directional selectivity and lack of aliasing.…”

Section: Introductionmentioning

confidence: 99%

Microscopic image classification via ℂWT-based covariance descriptors using Kullback-Leibler distance

Keskin

Çetin

Erşahin

et al. 2012

2012 IEEE International Symposium on Circuits and Systems

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Abstract-In this paper, we present a novel method for classification of cancer cell line images using complex waveletbased region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. CWT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method.

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Video Coding Technique Using Swarm Intelligence in 3-D Dual Tree Complex Wavelet Transform

Cited by 8 publications

References 16 publications

A multiplication-free framework for signal processing and applications in biomedical image analysis

A multiplication-free framework for signal processing and applications in biomedical image analysis

Video Coding Technique with Multi Objective Particle Swarm Optimization and EZW

Microscopic image classification via ℂWT-based covariance descriptors using Kullback-Leibler distance

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Video Coding Technique Using Swarm Intelligence in 3-D Dual Tree Complex Wavelet Transform

Cited by 8 publications

References 16 publications

A multiplication-free framework for signal processing and applications in biomedical image analysis

A multiplication-free framework for signal processing and applications in biomedical image analysis

Video Coding Technique with Multi Objective Particle Swarm Optimization and EZW

Microscopic image classification via &#x2102;WT-based covariance descriptors using Kullback-Leibler distance

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Microscopic image classification via ℂWT-based covariance descriptors using Kullback-Leibler distance