Multiple description image coding using contourlet transform

Naimi, Amina; Belloulata, Kamel

doi:10.1504/ijcaet.2019.096710

Cited by 2 publications

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“…In 2013, Gehrke et al [ 23 ] established a mathematical relationship between the coding gain and the DFB coefficient, and a new DFB was proposed based on the numerical optimization criterion of DFB coding gain in the lifting scheme, and its image compression performance outperformed the contourlet. In 2015, Naimi and Beloulata [ 24 ] proposed a multiple description image coding method based on contourlet transform, which can reliably transmit useful information to the encoder, so as to effectively code when packet loss occurs during the transmission of compressed code stream. They also demonstrated that the image coding performance surpassed wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

A combined HMM–PCNN model in the contourlet domain for image data compression

Yang

et al. 2020

PLoS ONE

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Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, but also outdoes the limitations of wavelet transform in representing high-dimensional singular data such as edges and contours. Therefore, researchers have been exploring new MGA-based image compression standards rather than the JPEG2000 standard. However, due to the difference in terms of the data structure, redundancy and decorrelation between wavelet and MGA, as well as the complexity of the coding scheme, so far, no definitive researches have been reported on the MGA-based image coding schemes. In addressing this problem, this paper proposes an image data compression approach using the hidden Markov model (HMM)/pulse-coupled neural network (PCNN) model in the contourlet domain. First, a sparse decomposition of an image was performed using a contourlet transform to obtain the coefficients that show the multiscale and multidirectional characteristics. An HMM was then adopted to establish links between coefficients in neighboring subbands of different levels and directions. An Expectation-Maximization (EM) algorithm was also adopted in training the HMM in order to estimate the state probability matrix, which maintains the same structure of the contourlet decomposition coefficients. In addition, each state probability can be classified by the PCNN based on the state probability distribution. Experimental results show that the HMM/PCNN-contourlet model proposed in this paper leads to better compression performance and offer a more flexible encoding scheme.

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Section: Introductionmentioning

confidence: 99%

A combined HMM–PCNN model in the contourlet domain for image data compression

Yang

et al. 2020

PLoS ONE

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“…In the previous methods, they usually adopt DCT orthogonal transform that lost some details of the image. Later, some works on the image transformation methods are studied [18], [19]. Reference [20] obtains ultra-fine description granularity by locally adaptive sparse representation of video signals.…”

Section: Introductionmentioning

confidence: 99%

KSVD-Based Multiple Description Image Coding

et al. 2019

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In this paper, we present a new multiple description coding scheme, which is based on a sparse dictionary training method called K singular value decomposition (KSVD). In the proposed scheme, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. The source processed by the KSVD becomes sparse, which can improve the coding efficiency. The proposed scheme is then applied to lapped transform-based multiple description image coding. Finally, image coding results show that the proposed scheme achieves a better performance than the current state-of-the-art multiple description coding methods. INDEX TERMS K singular value decomposition (KSVD), multiple description coding, sparse representation.

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Multiple description image coding using contourlet transform

Cited by 2 publications

References 0 publications

A combined HMM–PCNN model in the contourlet domain for image data compression

A combined HMM–PCNN model in the contourlet domain for image data compression

KSVD-Based Multiple Description Image Coding

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