Quantum multidimensional color image scaling using nearest-neighbor interpolation based on the extension of FRQI

2017 ◽  
Vol 31 (17) ◽  
pp. 1750184 ◽  
Author(s):  
Ri-Gui Zhou ◽  
Canyun Tan ◽  
Ping Fan
Keyword(s):  
Nearest Neighbor ◽  
Complexity Analysis ◽  
Color Image ◽  
Scaling Up ◽  
Quantum Image ◽  
Image Scaling ◽  
Lower Complexity ◽  
Scaling Down ◽  
Classical Computer ◽  
First Time

Reviewing past researches on quantum image scaling, only 2D images are studied. And, in a quantum system, the processing speed increases exponentially since parallel computation can be realized with superposition state when compared with classical computer. Consequently, this paper proposes quantum multidimensional color image scaling based on nearest-neighbor interpolation for the first time. Firstly, flexible representation of quantum images (FRQI) is extended to multidimensional color model. Meantime, the nearest-neighbor interpolation is extended to multidimensional color image and cycle translation operation is designed to perform scaling up operation. Then, the circuits are designed for quantum multidimensional color image scaling, including scaling up and scaling down, based on the extension of FRQI. In addition, complexity analysis shows that the circuits in the paper have lower complexity. Examples and simulation experiments are given to elaborate the procedure of quantum multidimensional scaling.

Applied Technology in Multidimensional Color Image Scaling Based on NASS in a Quantum System

2014 ◽  
Vol 1046 ◽  
pp. 411-414
Author(s):  
Hai Sheng Li ◽  
Kai Song
Keyword(s):  
Quantum System ◽  
Nearest Neighbor ◽  
Quantum Computer ◽  
Color Image ◽  
Quantum Circuit ◽  
Geometric Transformation ◽  
Superposition State ◽  
Image Scaling ◽  
Applied Technology ◽  
Scaling Down

In this study, an important geometric transformation, multidimensional color image scaling based on an n-qubit normal arbitrary superposition state (NASS), is put forward. In order to reduce the complexity of implementation of image scaling in a quantum system, nearest neighbor interpolation algorithm is chosen to implement image scaling up. And the corresponding quantum circuit of implementation is proposed. Finally, we discuss measurements of the part qubits of a NASS state to realize image scaling down. The paper explores theoretical and practical aspects of image processing on a quantum computer.

scholarly journals Scaling Up and Down of 3-D Floating-point Data in Quantum Computation

2021 ◽  
Author(s):  
Meiyu Xu ◽  
Dayong Lu ◽  
Xiaoyun Sun
Keyword(s):  
Quantum Computation ◽  
Scaling Up ◽  
Floating Point ◽  
Geometric Transformation ◽  
Double Precision ◽  
Quantum Image ◽  
Image Scaling ◽  
Trilinear Interpolation ◽  
Point Data ◽  
Floating Point Numbers

Abstract In the past few decades, quantum computation has become increasingly attractivedue to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes.In this paper, we present quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms compared to previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double precision or 128-bit extended-precision. Usually, we use nearest neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point numbers in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the circuits of quantum scaling up and down for 3-D floating-point data are designed.

scholarly journals Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

2021 ◽  
Author(s):  
Mehdi Alidokht ◽  
Samaneh Yazdani ◽  
Esmaeil Hadavandi ◽  
Saeed Chehreh Chelgani
Keyword(s):  
Laboratory Experiments ◽  
Scaling Up ◽  
Dense Medium ◽  
Support Vector ◽  
Coal Processing ◽  
Separation Device ◽  
Modeling Methods ◽  
Machine Learning Methods ◽  
Coal Washery ◽  
First Time

AbstractTri-flo cyclone, as a dense-medium separation device, is one of the most typical environmentally friendly industrial techniques in the coal washery plants. Surprisingly, no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses (yield and recovery). To fill this gap, this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method (boosted-neural network “BNN”) which is able to linearly and nonlinearly assess multivariable correlations among all variables, rank them based on their effectiveness and model their produced responses. These assessments and modeling were considered a new concept called “Conscious Laboratory (CL)”. CL can markedly decrease the number of laboratory experiments, reduce cost, save time, remove scaling up risks, expand maintaining processes, and significantly improve our knowledge about the modeled system. In this study, a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators. Well-known machine learning methods, random forest, and support vector regression were developed to validate BNN outcomes. The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods. In a sentence, generating a novel BNN within the CL concept can apply in various energy and coal processing areas, fill gaps in our knowledge about possible interactions, and open a new window for plants' fully automotive process.

Quantum color image watermarking based on fast bit-plane scramble and dual embedded

2018 ◽  
Vol 16 (07) ◽  
pp. 1850060 ◽  
Author(s):  
Ri-Gui Zhou ◽  
Peng Liu Yang ◽  
Xing Ao Liu ◽  
Hou Ian
Keyword(s):  
Nearest Neighbor ◽  
Color Image ◽  
Interpolation Method ◽  
Image Watermarking ◽  
Extraction Process ◽  
Watermark Image ◽  
Color Image Watermarking ◽  
Encryption Algorithms ◽  
Bit Plane ◽  
Watermarking Scheme

Most of the studied quantum encryption algorithms are based on square images. In this paper, based on the improved novel quantum representation of color digital images model (INCQI), a quantum color image watermarking scheme is proposed. First, INCQI improved from NCQI is used to represent the carrier and watermark images with the size [Formula: see text] and [Formula: see text], respectively. Secondly, before embedding, the watermarking needs to be preprocessed. That is, the watermark image with the size of [Formula: see text] with 24-qubits color information is disordered by the fast bit-plane scramble algorithm, and then further expanded to an image with the size [Formula: see text] with 6-qubits pixel information by the nearest-neighbor interpolation method. Finally, the dual embedded algorithm is executed and a key image with 3-qubits information is generated for retrieving the original watermark image. The extraction process of the watermark image is the inverse process of its embedding, including inverse embedding, inverse expand and inverse scrambling operations. To show that our method has a better performance in visual quality and histogram graph, a simulation of different carrier and watermark images are conducted on the classical computer’s MATLAB.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

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