scholarly journals Image Compression Based on Arithmetic Coding Algorithm

2021 ◽  
pp. 329-334
Author(s):  
Ruaa Ibrahim Yousif ◽  
Nassir Hussein Salman
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Rapid Development ◽  
Arithmetic Coding ◽  
Compact Form ◽  
Color Model ◽  
Cosine Transform ◽  
High Image Quality ◽  
The Past

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.

scholarly journals Pre and Postprocessing for JPEG to Handle Large Monochrome Images

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 255 ◽  
Author(s):  
Walaa Khalaf ◽  
Abeer Al Gburi ◽  
Dhafer Zaghar
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Rapid Development ◽  
Reconstructed Image ◽  
Hyperbolic Function ◽  
Image Size ◽  
New Techniques ◽  
Large Size

Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.

scholarly journals Improving Fractal Image Compression Scheme through Quantization Operation

2007 ◽  
Vol 4 (2) ◽  
pp. 330-337
Author(s):  
Baghdad Science Journal
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
New Method ◽  
Compression Scheme ◽  
Fractal Image Compression ◽  
High Image Quality ◽  
Fractal Image ◽  
Separate Step ◽  
Transform Coefficients

We explore the transform coefficients of fractal and exploit new method to improve the compression capabilities of these schemes. In most of the standard encoder/ decoder systems the quantization/ de-quantization managed as a separate step, here we introduce new way (method) to work (managed) simultaneously. Additional compression is achieved by this method with high image quality as you will see later.

scholarly journals DESIGN OF NEURAL NETWORK AS DATA FLOW MODEL FOR IMAGE COMPRESSION

2013 ◽  
pp. 195-199
Author(s):  
DANESHWARI I. HATTI ◽  
SAVITRI RAJU ◽  
MAHENDRA M. DIXIT
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
Convergence Time ◽  
Training Algorithms ◽  
Hardware Complexity ◽  
Cosine Transform ◽  
Hidden Neurons

In digital communication bandwidth is essential parameter to be considered. Transmission and storage of images requires lot of memory in order to use bandwidth efficiently neural network and Discrete cosine transform together are used in this paper to compress images. Artificial neural network gives fixed compression ratio for any images results in fixed usage of memory and bandwidth. In this paper multi-layer feedforward neural network has been employed to achieve image compression. The proposed technique divides the original image in to several blocks and applies Discrete Cosine Transform (DCT) to these blocks as a pre-process technique. Quality of image is noticed with change in training algorithms, convergence time to attain desired mean square error. Compression ratio and PSNR in dB is calculated by varying hidden neurons. The proposed work is designed using MATLAB 7.10. and synthesized by mapping on Vertex 5 in Xilinx ISE for understanding hardware complexity. Keywords - backpropagation, Discrete

scholarly journals Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

2021 ◽  
Vol 11 (2) ◽  
pp. 122-134
Author(s):  
Saleh Alshehri
Keyword(s):  
Neural Networks ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
Discrete Cosine Transformation ◽  
Compression Technique ◽  
Cosine Transform ◽  
Image Measure ◽  
Transform Coefficients ◽  
Execution Times

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.

CRITICAL REVIEW OF FRACTAL IMAGE COMPRESSION

1995 ◽  
Vol 06 (01) ◽  
pp. 47-66 ◽  
Author(s):  
HARRI RAITTINEN ◽  
KIMMO KASKI
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Critical Review ◽  
Quality Measures ◽  
Fractal Image Compression ◽  
Fractal Compression ◽  
New Methods ◽  
Fractal Image ◽  
Image Quality Measures

In this paper, fractal compression methods are reviewed. Three new methods are developed and their results are compared with the results obtained using four previously published fractal compression methods. Furthermore, we have compared the results of these methods with the standard JPEG method. For comparison, we have used an extensive set of image quality measures. According to these tests, fractal methods do not yield significantly better compression results when compared with conventional methods. This is especially the case when high coding accuracy (small compression ratio) is desired.

scholarly journals Is a thin diameter ureteroscope feasible for image guided intravascular procedures?

2020 ◽  
Vol 6 (3) ◽  
pp. 591-594
Author(s):  
Axel Boese ◽  
Michael Friebe
Keyword(s):  
Blood Flow ◽  
Image Quality ◽  
Imaging System ◽  
Low Cost ◽  
Clinical Routine ◽  
High Image Quality ◽  
Image Guided ◽  
The Past ◽  
Long Time ◽  
Feasibility Test

AbstractVascular endoscopic imaging is known for a long time but has never made its way into clinical routine. Reasons for that are the complexity, lack of low-cost portable systems, and the lack of suitable endoscopes providing high image quality with small dimensions. In addition, an interruption of the blood flow caused by the device and the opacity of blood are difficult to manage. In the past we have already developed ideas to overcome these difficulties and now we present a feasibility test of a thin diameter ureteroscope for observation of vascular procedures. The imaging system was tested in a phantom where side branches were explored, a stent was placed and a simulated aneurysm coiled.

scholarly journals ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN AERIAL IMAGES

Aviation ◽  
2007 ◽  
Vol 11 (4) ◽  
pp. 24-28
Author(s):  
Darius Mateika ◽  
Romanas Martavicius
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Quality Evaluation ◽  
Aerial Images ◽  
Aerial Image ◽  
Image Quality Evaluation ◽  
Compression Effect ◽  
Evaluation Algorithm ◽  
The Mean

In modern photomap systems, images are stored in centralized storage. Choosing a proper compression format for the storage of an aerial image is an important problem. This paper analyses aerial image compression in popular compression formats. For the comparison of compression formats, an image quality evaluation algorithm based on the calculation of the mean exponent error value is proposed. An image quality evaluation experiment is presented. The distribution of errors in aerial images and explanation of the causes for worse than usual compression effect are analysed. An integrated solution for the aerial image compression problem is proposed and the compression format most suitable for aerial images is specified.

scholarly journals The Arithmetic Coding and Hybrid Discrete Wavelet and Cosine Transform Approaches in Image Compression

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Nassir H. Salman ◽  
S. Rafea
Keyword(s):  
Magnetic Resonance Imaging ◽  
Wavelet Transform ◽  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Arithmetic Coding ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Resonance Imaging ◽  
Signal To Noise

Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurrence through the sub intervals between the range 0 and 1. Finally, the stream of compressed tables is reassembled for decompressing (image restoration). The results showed a compression gain of 10-12% and less time consumption when applying this type of coding to each block rather than the entire image. To improve the compression ratio, the second approach was used based on the YCbCr colour model. In this regard, images were decomposed into four sub-bands (low-low, high-low, low-high, and high-high) by using the discrete wavelet transform compression algorithm. Then, the low-low sub-band was transmuted to frequency components (low and high) via discrete wavelet transform. Next, these components were quantized by using scalar quantization and then scanning in a zigzag way. The compression ratio result is 15.1 to 27.5 for magnetic resonance imaging with a different peak signal to noise ratio and mean square error; 25 to 43 for X-ray images; 32 to 46 for computed tomography scan images; and 19 to 36 for magnetic resonance imaging brain images. The second approach showed an improved compression scheme compared to the first approach considering compression ratio, peak signal to noise ratio, and mean square error.

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