Image content matched directional filters for image coding based on contourlet transform

Gehrke, Tobias; Greiner, Thomas

doi:10.1109/pcs.2013.6737727

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The selection of subbands was accomplished by a mean-shift-based decision procedure, and the embedded subband coding with optimized truncation (ESCOT) coding was adopted, which reduced computational complexity and achieved better image compression performance [22]. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Yang

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

Image content matched directional filters for image coding based on contourlet transform

Cited by 1 publication

References 12 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

Contact Info

Product

Resources

About