scholarly journals Two-dimensional satellite image compression using compressive sensing

2022 ◽  
Vol 12 (1) ◽  
pp. 311
Author(s):  
Gunasheela Keragodu Shivanna ◽  
Haranahalli Shreenivasamurthy Prasantha
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Satellite Image ◽  
Research Community ◽  
Reconstruction Accuracy ◽  
Promising Technique ◽  
Compression Technique ◽  
Satellite Imaging ◽  
Compression Algorithms ◽  
Ground Station

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.

Quantization Encoding Algorithm Based Satellite Image Compression

2017 ◽  
Vol 8 (3) ◽  
pp. 740
Author(s):  
Anand M ◽  
V. Mathivananr
Keyword(s):  
Image Compression ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Digital Data ◽  
Storage Space ◽  
Compression Process ◽  
Compression Technique ◽  
Transmission Bandwidth ◽  
Image Technique

In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.

scholarly journals A native enhanced elastic extension tables multi-tenant database

2020 ◽  
Vol 10 (6) ◽  
pp. 6618
Author(s):  
Magy El Banhawy ◽  
Walaa Saber ◽  
Fathy Amer
Keyword(s):  
Image Compression ◽  
Digital Image ◽  
Compression Technique ◽  
Compression Algorithms ◽  
Fundamental Factor ◽  
Redundant Data ◽  
Rgb Images ◽  
The Common ◽  
Digital Image Compression

A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.

scholarly journals An Efficient Pass Parallel SPIHT based Image Compression using Double Density Dual Tree Complex Wavelet Transform for WSN

2019 ◽  
Vol 8 (12) ◽  
pp. 2762-2768
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Image Compression ◽  
Wavelet Transforms ◽  
Computation Time ◽  
Set Partitioning ◽  
Complex Wavelet Transform ◽  
Compression Technique ◽  
Compression Algorithms ◽  
Complex Wavelet

For the past two decades, wavelet based image compression algorithms for Wireless Sensor Network (WSN) has gained broad attention than that of the spatial based image compression algorithms. In that, Dual Tree Complex Wavelet Transforms (DTCWT) has provided better results in terms of image quality and high compression rate. However, the selection of DTCWT based image compressions for various WSN based applications is not practically suitable, due to the major limitations of WSN such as, low bandwidth, low energy consumption and storage space. Therefore, an attempt has been made in this paper to develop image compression through simulation by considering the modified block based pass parallel Set Partitioning In Hierarchical Trees (SPIHT) with Double Density Dual Tree Complex Wavelet Transform (DDDTCWT) for compressing the WSN based images. In addition, bivariate shrink method is also adopted with the DDDTCWT to obtain better image quality within less computation time. It is observed through simulation results that above mentioned proposed technique provides better performance than that of existing compression technique

scholarly journals Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements

2020 ◽  
Vol 9 (3) ◽  
pp. 2569-2576
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Hyperspectral Image ◽  
Satellite Image ◽  
Research Work ◽  
Reconstruction Algorithm ◽  
Hyperspectral Data ◽  
Sparse Reconstruction ◽  
Total Variation Regularization ◽  
Reconstruction Process

Hyperspectral image compression using compressive sensing technique is very much important in the area of satellite image compression because it can greatly en hance the compression rate. The research work proposes a novel data adaptable sparse reconstruction algorithm for the reconstruction of hyperspectral images from compressive sensing measurements. In the proposed algorithm, compressive sensing technique is used for the compression of HSIs, where Gaussian i.i.d. matrix is used to generate compressive sensing measurements. The algorithm solves the optimization problem containing total variation regularization and data adaptable parameter terms. The regularization terms are added to provide hyperspectral data characteristics as priors into the objective function. The addition of priors helps in convergence of the algorithm into the desired solution. The algorithm alternatively reconstructs the end member matrix and abundance matrix instead of reconstructing the entire HSI at once, thereby reducing the computational complexity at the reconstruction process. To nullify the effect of modelling errors, debiasing is performed.

scholarly journals An Improved Image Compression Technique Using EZW and SPHIT Algorithms

2019 ◽  
Vol 32 (2) ◽  
pp. 145
Author(s):  
Aqeel K. Kadhim ◽  
Abo Bakir S. Merchany ◽  
Ameen Babakir
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
High Performance ◽  
Input Image ◽  
Compression Technique ◽  
Compression Efficiency ◽  
Compression Algorithms ◽  
Still Image ◽  
Significant Compression

 Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image. The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.

scholarly journals Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements

2020 ◽  
Vol 9 (3) ◽  
pp. 2569-2576
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Hyperspectral Image ◽  
Satellite Image ◽  
Research Work ◽  
Reconstruction Algorithm ◽  
Hyperspectral Data ◽  
Sparse Reconstruction ◽  
Total Variation Regularization ◽  
Reconstruction Process

Hyperspectral image compression using compressive sensing technique is very much important in the area of satellite image compression because it can greatly en hance the compression rate. The research work proposes a novel data adaptable sparse reconstruction algorithm for the reconstruction of hyperspectral images from compressive sensing measurements. In the proposed algorithm, compressive sensing technique is used for the compression of HSIs, where Gaussian i.i.d. matrix is used to generate compressive sensing measurements. The algorithm solves the optimization problem containing total variation regularization and data adaptable parameter terms. The regularization terms are added to provide hyperspectral data characteristics as priors into the objective function. The addition of priors helps in convergence of the algorithm into the desired solution. The algorithm alternatively reconstructs the end member matrix and abundance matrix instead of reconstructing the entire HSI at once, thereby reducing the computational complexity at the reconstruction process. To nullify the effect of modelling errors, debiasing is performed

Efficient Image Compression Algorithms Using Evolved Wavelets

2011 ◽  
Vol 1 (4) ◽  
pp. 1-8
Author(s):  
G. Chenchu Krishnaiah ◽  
T. Jayachandraprasad ◽  
M.N. Giri Prasad
Keyword(s):  
Image Compression ◽  
Compression Algorithms

ESTIMATION OF HYBRID IMAGE COMPRESSION ALGORITHMS FOR UNDERWATER IMAGES

2018 ◽  
Vol 5 (4) ◽  
pp. 34
Author(s):  
R. PANDIAN ◽  
KUMARI S. LALITHA ◽  
KUMAR R. RAJA ◽  
RAVIKUMAR D. N. S. ◽  
◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Algorithms
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