Handbook of Data Compression

Salomon, David; Motta, Giovanni

doi:10.1007/978-1-84882-903-9

Cited by 329 publications

(254 citation statements)

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“…From Table ( 6) to Table (11) explained CR and PSNR for the second method (L*L kernel) which is suggested for storing data. 4*4, 8*8 and 16*16 kernels are tested for four images and provided lower distortion.…”

Section: Discussionmentioning

confidence: 99%

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A Proposed Lossy Image Compression based on Multiplication Table

Mahdi

Hassan

2017

KJAR

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Lately, Internet improved in the various trends, especially, the use of the image increased due to the daily use in several scopes like social media (Facebook, Twitter, WhatsApp, etc.), connected devices (sensor, IP camera, Internet of Things (IoT) Internet of Everything (IoE), etc) and smart phone devices that users interchanged images estimated in the billions. So, images issues in internet can be summarized into two criteria, the first criteria is considered with transmit image size. The second criteria is considered with low bandwidth through transmission. This paper exhibits a methodology for image compression using an idea of multiplication Table. The suggested algorithm helpful in realizing a preferable achievement by presenting a high Compression Ratio, preserve image quality with a high PSNR, small losing in the original image and efficiently in running time.

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Section: Discussionmentioning

confidence: 99%

“…During the compression process in this method, there is a little loss of data, so it is called irreversible compression. This loss of data will not perceive by the human [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Proposed Lossy Image Compression based on Multiplication Table

Mahdi

Hassan

2017

KJAR

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“…It is easy to verify that the uncompressed storage in this case may require about 315 MB per month (100 sensor * 8 bytes (time) * 2 bytes (measured value) * 365 days * 24 hours * 120 sample / hour for one sensor at 30 -second sampling rate). As far as, the number of sensors in complex systems can be significantly bigger, and the storage period may be several years, it is clear that to solve the problem of fast compression and decompression of large data sets in real-time, the traditional approaches, for example in [8][9][10][11][12][13][14][15][16][17], may be not helpful. This is explained by the fact that purely statistical methods are universal, as a rule, the block processing of the input data, whereas the dictionary methods of compression are based on the adaptation of some models without taking into account the real properties of the recorded data stream.…”

Section: General Definition Of the Problemmentioning

confidence: 99%

A Comparison of Data Compression Methods for Solving Problems of Temperature Monitoring

2016

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Abstract.The paper considers the problem of large processing huge amounts of data for temperature monitoring of man-made and natural objects associated with the lack of data compression efficiency in real time when they are transferred and stored in the presence of anomalies in the information signal in the form of sudden changes and outlier. The solutions of existing methods were described and new approaches were proposed. The results of experimental comparison of proposed and known solutions are included.

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“…In order to compress the configuration space and then decompress specific regions of interest, we initially divide the configuration space into basic building blocks. There are various techniques that can be used for data compression (see [8,9] for extensive surveys on data 1. Lossless data compression methods -these methods are rather limited and even sophisticated ones (e.g.…”

Section: Climbing Robotmentioning

confidence: 99%

C-Space Compression for Robots Motion Planning

Medina

Taitz

Moshe

et al. 2013

International Journal of Advanced Robotic Systems

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Exact motion planning for hyper-redundant robots under complex constraints is computationally intractable. This paper does not deal with the optimization of motion planning algorithms, but rather with the simplification of the configuration space presented to the algorithms. We aim to reduce the configuration space so that the robot's embedded motion planning system will be able to store and access an otherwise immense data file. We use a n-DCT compression algorithm together with a Genetic based compression algorithm, in order to reduce the complexity of motion planning computations and reduce the need for memory. We exemplify our algorithm on a hyperredundant worm-like climbing robot with six degrees of freedom (DOF).

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Handbook of Data Compression

Cited by 329 publications

References 0 publications

A Proposed Lossy Image Compression based on Multiplication Table

A Proposed Lossy Image Compression based on Multiplication Table

A Comparison of Data Compression Methods for Solving Problems of Temperature Monitoring

C-Space Compression for Robots Motion Planning

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