Image Compression Using Contourlet Transform

Al-Rawi, Zainab N. Abdulhameed; Hatem, Haraa Raheem; Ali, Israa Hazem

doi:10.1109/aicis.2018.00053

Cited by 3 publications

References 4 publications

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Complex Contourlet Transform Domain Based Image Compression

Saranya

Shrinidhi

Bargavi

2021

Intelligent Sustainable Systems

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In most of the important applications like transmission and storage purposes, the important technique used worldwide is image compression method. In general, the digital image contains an immense size of information, and it is an essential need to remove the data or information before transmission and storage. This work process the image compression method using the Raspberry Pi processor. The processor helps to retain a huge amount of image or data information with better image quality. Raspberry Pi supercomputer permits the execution with help of contourlet (families) transform (CT) using python for image compression technique. The digital still images are focussed and captured at the given time using a Web camera that is connected to a Raspberry Pi supercomputer at an inaccessible place. Further, the image compression method ensures that storage capacity is good in the proposed method with good memory compatibility. Then, the target host receives the compressed image and displays the decompressed output. The image compression method is performed by using the complex contourlet transform, it quantizes the transformed matrix, and then performs the encoding process. Finally, the inverse complex contourlet transform is used for image decompression method in order to retrieve the image back.

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Complex Contourlet Transform Domain Based Image Compression

Saranya

Shrinidhi

Bargavi

2021

Intelligent Sustainable Systems

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Evaluation of OFDM over a noisy channel based on transmitting multimedia data

Abdulhameed,

Hamd,

Najm

et al. 2024

2ND INTERNATIONAL CONFERENCE FOR ENGINEERING SCIENCES AND INFORMATION TECHNOLOGY (ESIT 2022): ESIT2022 Conference Proceedings

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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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Image Compression Using Contourlet Transform

Cited by 3 publications

References 4 publications

Complex Contourlet Transform Domain Based Image Compression

Complex Contourlet Transform Domain Based Image Compression

Evaluation of OFDM over a noisy channel based on transmitting multimedia data

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

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