Research and Exploration on Static Image Compression Technology Based on JPEG2000

2014 ◽  
Vol 644-650 ◽  
pp. 4182-4186
Author(s):  
Hua Tian ◽  
Ming Jun Li ◽  
Huan Huan Liu
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Processing System ◽  
Coding System ◽  
Compression Process ◽  
Static Image ◽  
Compression Time ◽  
Speedup Ratio ◽  
Universal Applicability ◽  
Static Form

This article introduces GPU-accelerated image processing parallel computing technology into standard core coding system of JPEG2000 static image compression and accelerates and designs the image compression process using CUDA acceleration principle. It also establishes the algorithm of image pixel array layered and reconstruction coding and realizes the coding of this algorithm using VC software. In order to verify the effectiveness and universal applicability of the algorithm and procedures, this paper compresses four images of different sizes and pixels in the static form of the JPEG2000. Through the comparison of the compression time, we can find that GPU hardware image processing system has a higher speedup ratio. With the increase of pixel and size, speedup ratio gradually increased which means that GPU acceleration has good adaptability.

scholarly journals Biomedical Image Compression Techniques for Clinical Image Processing

2020 ◽  
Vol 16 (12) ◽  
pp. 133
Author(s):  
Abdul Khader Jilani Saudagar
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Image Compression ◽  
Image Coding ◽  
Medical Images ◽  
Focal Point ◽  
Clinical Image ◽  
Coding System ◽  
Quality Factors ◽  
Clinical Images

Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

Image Processing Technology for Aerial Camera Manipulator

2014 ◽  
Vol 644-650 ◽  
pp. 4072-4075
Author(s):  
Zheng Liang ◽  
Jian An Yuan
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Real Time ◽  
Processing System ◽  
Processing Technology ◽  
Storage Space ◽  
Image Information ◽  
Aircraft Systems ◽  
Wavelet Coding ◽  
Important Means

CCD aerial camera is one of the important means of obtaining the image information on the ground, it is through the collection, archiving, and reading to achieved the images acquisition. As the very large amounts of data of the images, it takes a lot of time far more than analysis and processing when archiving and reading, so that not only difficult achieve real-time detection and processing, but also causing a waste of storage space. Therefore, the research of image compression and other processing technology has become important particularly.This paper use the wavelet coding to get images compression for the problem, and design the image processing system of aerial camera manipulator. This system designed by embedded modular, and ARINC 429 bus to achieve communications between the camera and the aircraft systems, make compression to the images which captured by the camera, and deal with the compressed image as stored, local zoom in and out, etc.

Design and Hardware Implementation of Image Compression Denoising Based on Median Filter and Wavelet Transform

2014 ◽  
Vol 602-605 ◽  
pp. 3218-3222
Author(s):  
Wei Qu ◽  
Xiao Xin Sun
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Image Compression ◽  
Hardware Implementation ◽  
Median Filter ◽  
Processing System ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Denoising Method

An image compression denoising method based on median filter and wavelet transform is proposed in order to overcoming shortcomings of traditional methods of image processing in this paper. This method combined hardware parallelism with software technology is enable to achieve image compression denoising and take into account algorithm validation, and fast response of the system. An real-time image processing system is design by this method. Design and hardware implementation of fast median filtering algorithm based on EP1C12 FPGA chip is realized and software simulation of median filter and wavelet transform is done. The experimental results show that this system has advantages of fast response characteristic, less time consuming and high signal to noise ratio.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

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