Performance Analysis of the KMM Digital Image Compression Process

2020 ◽  
Author(s):  
Poth Miklos ◽  
Zeljen Trpovski ◽  
Tatjana Loncar-Turukalo
Keyword(s):  
Performance Analysis ◽  
Image Compression ◽  
Digital Image ◽  
Compression Process ◽  
Digital Image Compression

Learning Aided Digital Image Compression Technique for Medical Application

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.

Digital Image Compression Using a Genetic Algorithm

1999 ◽  
Vol 5 (6) ◽  
pp. 379-383 ◽  
Author(s):  
Cheng Yimin ◽  
Wang Yixiao ◽  
Sun Qibin ◽  
Sun Longxiang
Keyword(s):  
Genetic Algorithm ◽  
Image Compression ◽  
Digital Image ◽  
Digital Image Compression

Clinical Validation of Digital Image Compression Levels for Fetal Echocardiograms for Telemedicine

1998 ◽  
Vol 15 (4) ◽  
pp. 363-368
Author(s):  
DEREK A. FYFE ◽  
FRED EMGE ◽  
GREGORY JOHNSON ◽  
FRANCIS McCAFFREY ◽  
WILLIAM LUTEN ◽  
...  
Keyword(s):  
Image Compression ◽  
Digital Image ◽  
Clinical Validation ◽  
Digital Image Compression

Edge Detection Using Block Truncation Coding

2003 ◽  
Vol 17 (06) ◽  
pp. 951-966 ◽  
Author(s):  
Yu-Chen Hu ◽  
Chin-Chen Chang
Keyword(s):  
Computational Complexity ◽  
Image Compression ◽  
Edge Detection ◽  
Digital Image ◽  
Block Type ◽  
Detection Scheme ◽  
Block Truncation Coding ◽  
Bit Plane ◽  
Digital Image Compression ◽  
Simple Block

In this paper, a new edge detection scheme based on block truncation coding (BTC) is proposed. As we know, the BTC is a simple and fast scheme for digital image compression. To detect an edge boundary using the BTC scheme, the bit plane information of each BTC-compressed block is exploited, and a simple block type classifier is introduced. The experimental results show that the proposed scheme clearly detects the edge boundaries of digital images while requiring very little computational complexity. Meanwhile, the edge detection process can be incorporated into all BTC variant schemes. In other words, the newly proposed scheme provides a good approach for the detection of edge boundaries using block truncation coding.

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