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A combined HMM-PCNN model in the contourlet domain for image data compression
- Source :
- PLoS ONE, Vol 15, Iss 8, p e0236089 (2020), PLoS ONE
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- 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.
- Subjects :
- Computer science
Image Processing
Markov models
02 engineering and technology
Diagnostic Radiology
0302 clinical medicine
Wavelet
Animal Cells
Medicine and Health Sciences
0202 electrical engineering, electronic engineering, information engineering
Hidden Markov models
Hidden Markov model
Tomography
Data Management
Neurons
Coding Mechanisms
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Radiology and Imaging
Wavelet transform
Sparse approximation
computer.file_format
Markov Chains
Physical sciences
JPEG 2000
Engineering and Technology
Medicine
020201 artificial intelligence & image processing
Cellular Types
Algorithms
Research Article
Image compression
Data compression
Computer and Information Sciences
Imaging Techniques
Science
Wavelet Analysis
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Neuroimaging
Research and Analysis Methods
03 medical and health sciences
Diagnostic Medicine
Decorrelation
Computational Neuroscience
business.industry
Biology and Life Sciences
Computational Biology
Probability theory
Pattern recognition
Cell Biology
Data Compression
Contourlet
Computed Axial Tomography
Cellular Neuroscience
Signal Processing
Neural Networks, Computer
Artificial intelligence
business
computer
Mathematics
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 15
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....2fb9a36aa673f74f799a353e1b3143b5