Dyadic wavelet for image coding implementation on a Xilinx MicroBlaze processor: Application to neutron radiography

Saadi, Slami; Touiza, Maamar; Kharfi, Fayçal; Guessoum, Abderrezak

doi:10.1016/j.apradiso.2013.08.001

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…The aim of this section is to describe a hardware accelerator based on the Gabor filter. The proposed architecture is composed by Microblaze soft-core processors interfaced with shared memory using the AXI4 bus [46]. All processor elements, on-chip memories, and the AXI4 bus are clocked at 100 MHz.…”

Section: Experiments Resultsmentioning

confidence: 99%

An Automated Surveillance System Based on Multi-Processor and GPU Architecture

Ayed

Elkosantini

Abid

2017

Eng. Technol. Appl. Sci. Res.

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Video surveillance systems are a powerful tool applied in various systems. Traditional systems based on human vision are to be avoided due to human errors. An automated surveillance system based on suspicious behavior presents a great challenge to developers. Such detection is a rather complex procedure and also a rather time-consuming one. An abnormal behavior could be identified by: actions, faces, route, etc. The definition of the characteristics of an abnormal behavior still present a big problem. This paper proposes a specific architecture for a surveillance system. The aim is to accelerate the system and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section illustrates the results with comparison of some of the most recent approaches.

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Section: Experiments Resultsmentioning

confidence: 99%

An Automated Surveillance System Based on Multi-Processor and GPU Architecture

Ayed

Elkosantini

Abid

2017

Eng. Technol. Appl. Sci. Res.

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“…Saadi et al [19] proposed a mixed software/hardware implementation of 2-D signals encoder/decoder utilizing dyadic DWT in light of quadrature mirror filters utilizing quick wavelet Mallat's algorithm. This work was composed and aggregated on the embedded development kit EDK6.3i and the synthesis software ISE6.3i, which was accessible with Xilinx Virtex-IIV2MB1000 FPGA.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Anasuodei¹,

Eleonu²

2021

AJCST

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Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

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“…In 2013, Saadi et al [16] proposed a mixed software/hardware implementation of 2-D signals encoder/ decoder utilizing dyadic DWT in light of quadrature mirror filters utilizing quick wavelet Mallat's algorithm. This work is composed and aggregated on the embedded development kit EDK6.3i and the synthesis software ISE6.3i, which is accessible with Xilinx Virtex-IIV2MB1000 FPGA.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Compound Image Compression Using Optimal Discrete Wavelet Transform and Run Length Encoding Techniques

Rajan

Fred²

2017

Journal of Intelligent Systems

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Reduction in file size leads to reduction in the number of bits required to store it. When data is compressed, it must be decompressed into its original form bit for bit. Compound images are defined as images that contain a combination of text, natural (photo) images and graphic images. Here, compression is the process of reducing the amount of data required to represent information. Image compression is done on the basis of various loss and lossless compression algorithms. This research work deals with the preprocessing and transformations used to compress a compound image to produce a high compression ratio (CR), less compression time and so on. In the compression process the images are considered for preprocessing and discrete wavelet transform with adaptive particle swarm optimization process. The purpose of this optimization technique is to optimize the wavelet coefficient in Harr wavelet for improving the CR value. In the image compression process, run length coding is used to compress the compound images. Based on this technique, it produces minimum CR and less computation time of compound images.

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Dyadic wavelet for image coding implementation on a Xilinx MicroBlaze processor: Application to neutron radiography

Cited by 4 publications

References 13 publications

An Automated Surveillance System Based on Multi-Processor and GPU Architecture

An Automated Surveillance System Based on Multi-Processor and GPU Architecture

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

An Efficient Compound Image Compression Using Optimal Discrete Wavelet Transform and Run Length Encoding Techniques

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