scholarly journals Image Compression using Orthogonal Wavelets Viewed from Peak Signal to Noise Ratio and Computation Time

2012 ◽  
Vol 47 (4) ◽  
pp. 25-34 ◽  
Author(s):  
P. M.K.Prasad ◽  
Prabhakar Telagarapu ◽  
G. Uma Madhuri
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Signal To Noise ◽  
Orthogonal Wavelets ◽  
Noise Ratio

scholarly journals Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

2019 ◽  
Vol 8 (6) ◽  
pp. 23-37
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Huffman Coding ◽  
Signal To Noise ◽  
Codebook Design ◽  
Tlbo Algorithm ◽  
Noise Ratio ◽  
Selection Of

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

Vector Quantization for Satellite Image Compression

2016 ◽  
Vol 5 ◽  
pp. 22
Author(s):  
Sanjith Sathya Joseph ◽  
R. Ganesan
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Signal To Noise ◽  
Transmission Process ◽  
Noise Ratio ◽  
Time Required

Image compression is the process of reducing the size of a file without humiliating the quality of the image to an unacceptable level by Human Visual System. The reduction in file size allows as to store more data in less memory and speed up the transmission process in low bandwidth also, in case of satellite images it reduces the time required for the image to reach the ground station. In order to increase the transmission process compression plays an important role in remote sensing images.  This paper presents a coding scheme for satellite images using Vector Quantization. And it is a well-known technique for signal compression, and it is also the generalization of the scalar quantization.  The given satellite image is compressed using VCDemo software by creating codebooks for vector quantization and the quality of the compressed and decompressed image is compared by the Mean Square Error, Signal to Noise Ratio, Peak Signal to Noise Ratio values.

Optimization Before Biomedical Image Compression Using CLAHE and DCS

2018 ◽  
Vol 7 (3.27) ◽  
pp. 236
Author(s):  
Satyawati S. Magar ◽  
Bhavani Sridharan
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Image Enhancement ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Biomedical Image ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

In current years, improving the Compression Ratio (CR) in medical imaging is essential and becomes big challenge in the field of biomedical. In that direction we have done optimization before biomedical image compression. For the same we have used the image enhancement techniques. For the enhancement of an image we have used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Decorrelation Stretch (DCS) algorithms. By optimizing an image before compression we have achieved better Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) than existing methods of an image compression. Mainly results are compared with Oscillation Concept method of an image compression with and without optimization.  

scholarly journals When Choosing the Best Subset Is Not the Best Choice

2021 ◽  
Author(s):  
Moritz Hanke ◽  
Louis Dijkstra ◽  
Ronja Foraita ◽  
Vanessa Didelez
Keyword(s):  
Variable Selection ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Signal To Noise ◽  
Wide Range ◽  
Noise Ratio ◽  
Low Dimensional ◽  
Measure Of Performance

Abstract Background: Variable selection in linear regression settings is a much discussed problem. Best subset selection (BSS) is often considered as an intuitively appealing ‘gold standard’, with its use being restricted mainly by its N P-hard nature. Instead, alternatives such as the least absolute shrinkage and selection operator (Lasso) or the elastic net (Enet) have become methods of choice in high-dimensional settings. A recent proposal represents BSS as a mixed integer optimization problem so that much larger problems have become feasible in reasonable computation time. This has been exploited to study the prediction performance of BSS and its competitors. Here, we present an extensive simulation study assessing, instead, the variable selection performance of BSS compared to forward stepwise selection (FSS), Lasso and Enet. The analysis considers a wide range of settings that are challenging with regard to dimensionality, signal-to-noise ratio and correlations between relevant and irrelevant direct predictors. As measure of performance we used the best possible F1 score for each method so as to ensure a fair comparison irrespective of any criterion for choosing the tuning parameters.Results: Somewhat surprisingly, it was only in settings where the signal-to-noise ratio was high and the variables were (nearly) uncorrelated that BSS reliably outperformed the other methods. This was the case even in low dimensional settings where the number of observations exceeded the number of variables by a factor of ten. Further, the FSS approach performed nearly identically to BSS. Conclusion: Our results shed a new light on the usual presumption of BSS being, in principle, the best choice for variable selection. More attention needs to be payed to the data generating process when considering variable selection methods. Especially for correlated variables, convex alternatives like Enet are not only faster but also appear to be more accurate in practical settings.

scholarly journals Image Compression using Modified Haar Wavelet-Base Vector Quantization

1970 ◽  
Vol 3 (1) ◽  
pp. 72-76
Author(s):  
Anusorn Jitkam ◽  
Satra Wongthanavasu
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Signal To Noise Ratio ◽  
Haar Wavelet ◽  
Compression Algorithm ◽  
Wavelet Base ◽  
Signal To Noise ◽  
Base Vector ◽  
Noise Ratio ◽  
Image Compression Algorithm

This research presents an image compression algorithm using modified Haar wavelet and vector quantization. For comparison purposes, a standard Haar wavelet with vector quantization and SPIHT, which is used in JPEG2000, are compared with the proposed method using Peak Signal-to-Noise Ratio (PSNR). The proposed method shows better results on average over the compared methods.

A Study of the Perceptually Weighted Peak Signal-To-Noise Ratio (WPSNR) for Image Compression

2019 ◽  
Author(s):  
Johannes Erfurt ◽  
Christian R. Helmrich ◽  
Sebastian Bosse ◽  
Heiko Schwarz ◽  
Detlev Marpe ◽  
...  
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Analisis Pengaruh Citra Gelap, Normal, Terang Terhadap Wavelet Orthogonal

2018 ◽  
Vol 9 (2) ◽  
pp. 93
Author(s):  
Novera Kristianti ◽  
Niwayan Purnawati ◽  
Bryand Rolando
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Memory Usage ◽  
Original Image ◽  
Signal To Noise ◽  
Orthogonal Wavelet ◽  
Compression Process ◽  
Normal Image ◽  
Noise Ratio

Abstract. An image is classified into dark, normal, and bright image. The images are grouped in the dark images according to the histogram and the mu value. An image consists of information and redundancies. The use of wavelet is considered effective in image compression and it does not only cut down the memory usage but also it makes devices work faster. In this study, an analysis in conducted on the influence of dark, normal, and bright images on the orthogonal wavelet. Peak Signal to Noise Ratio (PSNR) is used to compare 17 functions of wavelet orthogonal in the image compression of dark, normal, and bright images. PSNR is a measurement parameter commonly used for measuring the quality of image reconstruction which is then compared with the original image. Compression ratio is used to measure the reduction of the data size after the compression process. Based on the research on the dark, normal, and bright image, the findings reveal that bright image has got the lowest PNSR value at all image testing while the normal image has the highest PSNR value at the wavelet orthogonal application. Keywords : Image compression, Orthogonal wavelet, PSNR, compression ratio.Abstrak. Suatu citra dikelompokkan menjadi citra gelap, citra normal, dan citra terang. Pengelompokan citra menjadi warna gelap terlihat dari histogram dan nilai rerata intensitas (mu). Citra terdiri atas informasi dan redudansi. Penggunaan wavelet dinilai efektif dalam kompresi citra dan menurunkan penggunaan memori serta membuat perangkat menjadi lebih cepat. Pada penelitian ini, dilakukan analisis pengaruh citra gelap, citra normal, dan citra terang terhadap wavelet orthogonal. Peak Signal to Noise Ratio (PSNR) digunakan untuk membandingkan 17 fungsi wavelet orthogonal dalam kompresi citra gelap, citra normal, dan citra terang. PSNR adalah parameter ukur yang sering digunakan untuk pengukuran kualitas gambar rekonstruksi, yang lalu dibandingkan dengan gambar asli. Rasio kompresi digunakan untuk mengukur pengurangan ukuran data setelah proses kompresi. Berdasarkan penelitian pada citra gelap, citra normal, dan citra terang diperoleh bahwa citra terang menghasilkan nilai PSNR paling kecil untuk seluruh citra uji dan citra normal menghasilkan nilai PSNR paling besar dalam penerapan wavelet orthogonal. Kata kunci : Kompresi citra, Wavelet orthogonal, PSNR, rasio kompresi.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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