Threshold value for acceptable video quality using signal-to-noise ratio

2007 ◽  
Author(s):  
Mikko Vaahteranoksa ◽  
Tero Vuori
Keyword(s):  
Signal To Noise Ratio ◽  
Video Quality ◽  
Threshold Value ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Cooperative GPS and Neighbors Awareness Based Device Discovery for D2D Communication in in-Band Cellular Networks

2018 ◽  
Vol 7 (2.29) ◽  
pp. 700 ◽  
Author(s):  
O Hayat ◽  
R Ngah ◽  
Yasser Zahedi
Keyword(s):  
Cellular Networks ◽  
Channel Capacity ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
D2d Communication ◽  
Core Network ◽  
New Paradigm ◽  
Signal To Noise ◽  
Device Discovery ◽  
Noise Ratio

Device to Device (D2D) communication is a new paradigm for next-generation wireless systems to offload data traffic. A device needs to discover neighbor devices on the certain channel to initiate the D2D communication within the minimum period. A device discovery technique based on Global Positioning System (GPS) and neighbor awareness base are proposed for in-band cellular networks. This method is called network-centric approach, and it improves the device discovery efficiency, accuracy, and channel capacity. The differential code is applied to measure the signal to noise ratio of each discovered device. In the case that the signal to noise ratio (SNR) of two devices is above a specified threshold value, then these two devices are qualified for D2D communication. Two procedures are explored for device discovery; discovery by CN (core network) and eNB (evolved node B) cooperation with the help of GPS and neighbor awareness. Using ‘Haversine’ formula, SNR base distance is calculated. Results show an increment in the channel capacity relative to SNR obtained for each device.  

Quantitative Analysis of Noise Impact on Duffing Chaotic Detection System Using Lyapunov Characteristic Exponents

2011 ◽  
Vol 130-134 ◽  
pp. 1331-1337
Author(s):  
Wen Jing Hu ◽  
Zhi Zhen Liu ◽  
Zhi Hui Li
Keyword(s):  
Detection System ◽  
Signal To Noise Ratio ◽  
Duffing Oscillator ◽  
Threshold Value ◽  
System State ◽  
Signal To Noise ◽  
Phase Trajectories ◽  
Lyapunov Characteristic Exponents ◽  
Noise Ratio ◽  
System States

Performance of the Duffing oscillator to detect weak signals buried in heavy noise is analyzed quantitatively by LCEs. First in the case of noise, differential equations to compute LCE s are derived using RHR algorithm, so the quantitative criteria to identify system states are obtained. Then using LCEs, the threshold value of the forced periodic term is found accurately. Finally the system state and state change are analyzed using LCEs by keeping the threshold value and varying the noise intensity, and the minimum signal to noise ratio is determined. By contrast of phase trajectories and LCEs, it shows that phase trajectories disturbed by strong noise sometimes are ambiguous to our eyes, but through LCEs, the system state can be identified clearly and quantitatively especially in strong noise background. So the minimum signal to noise ratio can be obtained accurately.

scholarly journals An Image Filter Based on Multiobjective Genetic Algorithm and Shearlet Transformation

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Zhi-yong Fan ◽  
Quan-sen Sun ◽  
Ze-xuan Ji ◽  
Kai Hu
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Target Function ◽  
Threshold Value ◽  
Rician Noise ◽  
Signal To Noise ◽  
Multiobjective Genetic Algorithm ◽  
Noise Ratio ◽  
Magnetic Resonance Imaging Mri

Rician noise pollutes magnetic resonance imaging (MRI) data, making data’s postprocessing difficult. In order to remove this noise and avoid loss of details as much as possible, we proposed a filter algorithm using both multiobjective genetic algorithm (MOGA) and Shearlet transformation. Firstly, the multiscale wavelet decomposition is applied to the target image. Secondly, the MOGA target function is constructed by evaluation methods, such as signal-to-noise ratio (SNR) and mean square error (MSE). Thirdly, MOGA is used with optimal coefficients of Shearlet wavelet threshold value in a different scale and a different orientation. Finally, the noise-free image could be obtained through inverse wavelet transform. At the end of the paper, experimental results show that this proposed algorithm eliminates Rician noise more effectively and yields better peak signal-to-noise ratio (PSNR) gains compared with other traditional filters.

scholarly journals A Video Quality Testing : Review of Human Visual Aspect

2021 ◽  
Vol 6 (2) ◽  
pp. 85-93
Author(s):  
Andi Danang Krismawan ◽  
Lekso Budi Handoko
Keyword(s):  
Signal To Noise Ratio ◽  
Video Quality ◽  
Similarity Index ◽  
Visual Signal ◽  
Signal To Noise ◽  
Quality Metric ◽  
Index Measurement ◽  
Noise Ratio ◽  
Video Player ◽  
Visual Results

Various types of video player applications have been widely used by the community. The emergence of the latest version and a variety of features make people need to make a choice to use a video player application with a good visual level. The type of video that is often played is a file with an MP4 extension. This file type is not heavy but is usually intended for long file durations such as movies. In this paper, we will use a dataset in the form of a movie file with an MP4 extension. The video player applications used include VLC, Quick time, Potplayer, KMPLayer, Media Player Classic (MPC), DivX Player, ACG Player, Kodi, MediaMonkey. Through various empirical calculations, such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structutral Similarity Index Measurement (SSIM), Threshold F-ratio, Visual Signal to Noise Ratio (VSNR), Visual Quality Metric (VQM), and Multiscale - Structutral Similarity Index Measurement (MS-SSIM) has analyzed the visual capabilities of each video player application. Experimental results prove that the KMPlayer application gets the best visual results compared to other selected applications.

NMR spectroscopy threshold signal-to-noise ratio

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Petar Kolar ◽  
Lovro Blažok ◽  
Dario Bojanjac
Keyword(s):  
Nmr Spectroscopy ◽  
Communication Channel ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Standard Procedure ◽  
Threshold Value ◽  
Signal To Noise ◽  
Online Questionnaire ◽  
Spectroscopy System ◽  
Noise Ratio

Abstract Ever since noise was spotted and proven to cause problems for the transmission and detection of information through a communication channel, a standard procedure in the process of characterizing a detection system of the communication channel is to determine the level of the lowest detectable signal. In signal processing, this is usually done by determining the so-called threshold signal-to-noise ratio (SNR). This determination is especially important for the communication channels and systems that constantly operate with low-level signals. A good example of such a system is definitely the NMR spectroscopy system. However, to the authors’ knowledge, the threshold SNR value of NMR spectroscopy systems has not been determined yet. That is why the experts in the field of NMR spectroscopy were asked to assess, using an online questionnaire, which SNR level they considered to be the NMR threshold SNR level. Afterwards, the threshold value was calculated from the obtained data. Finally, it was compared to the existing rule of thumb and thus, a conclusion about its legitimacy was made. The described questionnaire is still available online (https://forms.gle/Y9hyDZ1v1iJoEbk27). This enables everyone to form their own opinion about the threshold SNR level, which the authors encourage the readers to do.

An automatic algorithm for the detection and the characterization of cloud boundaries from BAQUNIN LIDAR signals

2020 ◽  
Author(s):  
AnnaMaria Iannarelli ◽  
Marco Cacciani ◽  
Gabriele Mevi ◽  
Stefano Casadio ◽  
Annalisa Di Bernardino
Keyword(s):  
Boundary Layer ◽  
Temporal Resolution ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Temporal Variations ◽  
Cloud Layer ◽  
Spatial And Temporal Variations ◽  
Quality Analysis ◽  
Signal To Noise ◽  
Noise Ratio

<p>The lidar LIDAR system is widely used in atmospheric aerosol and boundary layer (BL) studies, and for the detection of cloud boundaries. However automatic and accurate identification of cloud top and bottom heights and BL height is not trivial, especially for low signal to noise ratio values, and for cloud layers below the top of BL, because of the disentanglement of cloud and aerosol contribution to LIDAR signal.</p><p>In this work, a signal threshold approach is presented, starting from the Range Corrected Signal (RCS) and using its spatial and temporal variations. The approach has been tested using one year of acquisitions of the elastic LIDAR hosted in the BAQUNIN (Boundary-layer Air QUality analysis using Network of INstruments) Supersite(https://www.baqunin.eu) with a spatial and temporal resolution of 7.5 m and 10 s, respectively.</p><p>A minimum threshold value T<sub>c</sub> applied to the RCS values allows detecting the presence of a cloud layer. This approach could be applied to each type of acquired LIDAR elastic signal, but depends on the specific LIDAR channel characteristics, in particular the signal to noise ratio.</p><p>RCS values obtained for each acquired profile and altitude could be considered as a two-dimensional matrix M. As first step the elements M<sub>ij</sub>>T<sub>c</sub> of this matrix are labeled as possible cloud elements.</p><p>Subsequently, the algorithm excludes from the calculation the elements M<sub>ij </sub>corresponding to spike values or affected by high noise considering the spatial and temporal variations of the RCS. A labeled element is confirmed to be a cloud element if the number of its labeled neighbors is above a selected percentage threshold T<sub>perc.</sub> The grid of elements considered as neighbors can be defined according to spatial and temporal resolution of the LIDAR acquisition.</p><p>Finally, bottom and top of cloud layers are retrieved as the altitude of first and last labeled elements of each cloud layer and profile.</p><p>The accuracy of the results depends on the spatial and temporal resolution of the acquired signal, considering the BAQUNIN LIDAR characteristics the best accuracy is 15 m and 20 s.</p><p>The same approach could be used to distinguish aerosol from cloud layers, using a different threshold value for the aerosol.</p><p>This method was tested for different atmospheric conditions and results are discussed in this work.</p>

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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