Reversible Watermarking on Stereo Audio Signals by Exploring Inter-Channel Correlation

2019 ◽  
Vol 11 (1) ◽  
pp. 29-45
Author(s):  
Yuanxin Wu ◽  
Wen Diao ◽  
Dongdong Hou ◽  
Weiming Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Reversible Watermarking ◽  
Experimental Results ◽  
Audio Signals ◽  
Signal To Noise ◽  
Channel Correlation ◽  
Noise Ratio ◽  
Embedding Distortion

A new reversible watermarking algorithm on stereo audio signals is proposed in this article. By utilizing correlations between two channels of audio signal, the authors segment one channel based on another one according to the smoothness. For each segmented sub-host sequence, they estimate its capacity and the corresponding embedding distortion firstly, and then select the optimal combinations of sub-host sequences for embedding. Experimental results indicate that the proposed algorithm can improve SNR (signal to noise ratio) for various kinds of capacity.

scholarly journals Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 265-268
Author(s):  
G.Manmadha Rao* ◽  
Raidu Babu D.N ◽  
Krishna Kanth P.S.L ◽  
Vinay B. ◽  
Nikhil V.
Keyword(s):  
Impulse Noise ◽  
Impulsive Noise ◽  
Rank Order ◽  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Window Size ◽  
Mean Value ◽  
Audio Signals ◽  
Signal To Noise ◽  
Noise Ratio

Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.

OPTICAL PROCESSING WITH PHOTOREFRACTIVE COMPOUND SEMICONDUCTORS

1992 ◽  
Vol 01 (03) ◽  
pp. 609-638
Author(s):  
L.J. CHENG ◽  
D.T.H. LIU ◽  
K.L. LUKE
Keyword(s):  
Semiconductor Lasers ◽  
Signal To Noise Ratio ◽  
Compound Semiconductors ◽  
Experimental Results ◽  
Response Compatibility ◽  
Cross Polarization ◽  
Optical Processing ◽  
Signal To Noise ◽  
Material Response ◽  
Noise Ratio

Photorefractive compound semiconductors are attractive for optical processing because of fast material response, compatibility with semiconductor lasers, and availability of cross polarization diffraction for enhancing signal-to-noise ratio. This paper presents a collection of recent experimental results on optical processing using photorefractive GaAs and InP. The results demonstrate the feasibility of using photorefractive compound semiconductors as dynamic holographic interaction media for optical processing applications.

scholarly journals Multiparametric approach to the assessment of muon tomographic results for the inspection of a full-scale container

2021 ◽  
Vol 136 (1) ◽  
Author(s):  
F. Riggi ◽  
M. Bandieramonte ◽  
U. Becciani ◽  
D. L. Bonanno ◽  
G. Bonanno ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Single Scattering ◽  
Full Scale ◽  
Experimental Results ◽  
Two Dimensional ◽  
Signal To Noise ◽  
Scattering Angle ◽  
Multiparametric Analysis ◽  
Noise Ratio ◽  
Cargo Containers

AbstractExperimental results from a dataset collected with a full-scale muon tomograph for the inspection of cargo containers were studied in a single scattering scenario with a multiparametric analysis based on the method of the Point Of Closest Approach (Poca). To search for high-Z materials, a 4 $$\hbox {dm}^3$$ dm 3 Pb block was positioned inside the volume to be inspected, in order to quantitatively investigate the appearance of the Poca signal. Signal-to-noise ratio and significance of the Poca signal were investigated by means of mono-dimensional spectra of the Poca components, for different values of the scattering angle between the incoming and outgoing muon tracks and with different angle-dependent weights. A systematic scan of two-dimensional maps was also carried out, as a strategy to search for possible enhancements to the Poca signal. A comparison was also done between the results obtained from the two half-volumes, one containing the Pb block and one left empty, to take into account the response of the detector and some aspects of the Poca strategy.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

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.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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