A native enhanced elastic extension tables multi-tenant database

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.

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Learning Aided Digital Image Compression Technique for Medical Application

Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-8654-0.ch019 ◽

2016 ◽

pp. 400-422

Author(s):

Kandarpa Kumar Sarma

Keyword(s):

Image Compression ◽

Digital Image ◽

Signal To Noise Ratio ◽

Medical Application ◽

Learning Systems ◽

Signal To Noise ◽

Compression Technique ◽

Compressed Images ◽

Digital Image Compression ◽

Efficiency And Reliability

The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.

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Frei-Chen bases based lossy digital image compression technique

Applied Computing and Informatics ◽

10.1016/j.aci.2019.12.004 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mahmood Al-khassaweneh ◽

Omar AlShorman

Keyword(s):

Image Compression ◽

Compression Rate ◽

High Quality ◽

Compression Technique ◽

Average Value ◽

Second Stage ◽

Compression Factor ◽

Digital Image Compression ◽

And Performance

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.

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Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons

Journal of Sensors ◽

10.1155/2016/3184840 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 5

Author(s):

Farhan Hussain ◽

Jechang Jeong

Keyword(s):

Image Compression ◽

Real Time ◽

Deep Neural Networks ◽

Compression Technique ◽

Training Time ◽

Time Compression ◽

Time Operation ◽

Real Time Operation ◽

Digital Image Compression

A compression technique for still digital images is proposed with deep neural networks (DNNs) employing rectified linear units (ReLUs). We tend to exploit the DNNs capabilities to find a reasonable estimate of the underlying compression/decompression relationships. We aim for a DNN for image compression purpose that has better generalization property and reduced training time and support real time operation. The use of ReLUs which map more plausibly to biological neurons, makes the training of our DNN significantly faster, shortens the encoding/decoding time, and improves its generalization ability. The introduction of the ReLUs establishes an efficient gradient propagation, induces sparsity in the proposed network, and is efficient in terms of computations making these networks suitable for real time compression systems. Experiments performed on standard real world images show that using ReLUs instead of logistic sigmoid units speeds up the training of the DNN by converging markedly faster. The evaluation of objective and subjective quality of reconstructed images also proves that our DNN achieves better generalization as most of the images are never seen by the network before.

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An Improved Image Compression Technique Using EZW and SPHIT Algorithms

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/32.2.2121 ◽

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.

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