Red-Green-Blue multi-channel quantum representation of digital images

Abdolmaleky, Mona; Naseri, Mosayeb; Batle, Josep Amengual i; Farouk, Ahmed; Gong, Li

doi:10.1016/j.ijleo.2016.09.123

Cited by 101 publications

(37 citation statements)

References 30 publications

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“…Based on the classical computer RGB model, Abdolmaleky et al extended the grayscale information in the NEQR to the color representation and proposed the QMCR [30]. Two entangled qubit sequences are used to encode the information of the position and color of each pixel.…”

Section: R Qmcrmentioning

confidence: 99%

“…Inspired by the three representations, a series of new quantum image representations have appeared. At present, quantum image representations mainly include NAQSS (Normal Arbitrary Quantum Superposition State) [14], QSMC&QSNC (Quantum States for M Colors and N Coordinates of An Image) [15], SQR (Simple Quantum Representation of Infrared Images) [16], QUALPI (Quantum Log-Polar Images) [17], NEQR (Novel Enhanced Quantum Representation) [18], CQIR (Caraiman's Quantum Image Representation) [19], MCQI (Multi-Channel Quantum Images) [20], INEQR(Improved NEQR) [21], GNEQR(A Generalized Model of NEQR) [22], NCQI(A Novel Quantum Representation of Color Digital Images) [23], BRQI (A Bitplane Representation of Quantum Images) [24], QRCI(A New Quantum Representation Model of Color Digital Images) [25], QRMMI (A Quantum Representation Model for Multiple Images) [26], QRMW (Quantum Representation of Multi Wavelength Images) [27], OCQR (An Optimized Quantum Representation for Color Digital Images) [28], FRQCI (An Improved FRQI Model) [29], QMCR (A Digital RGB Multi-Channel Representation for Quantum Colored Images) [30], IFRQI (Improved Flexible Representation of Quantum Images) [31], QBIR (A Quantum Block Image Representation) [32], OQIM (Order-encoded Quantum Image Model) [33], QIIR (Quantum Indexed Image Representation) [8], and DRQCI (A Double Quantum Color Images Representation Model) [34].…”

Section: Introductionmentioning

confidence: 99%

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A New Trend of Quantum Image Representations

Guo

Liu

et al. 2020

IEEE Access

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Quantum image processing will be an important issue in the age of quantum computers. Quantum image processing generally includes quantum image representations, quantum image processing algorithms, and quantum image measurement. Among them, quantum image representation is a very important part of quantum image processing. Therefore, it is of great significance for quantum image processing to study the representation of quantum images. First, we elaborate on the main quantum image representations and analyze, compare and summarize them. This paper focuses on the definition of these quantum image representations the preparation of quantum images and the number of quantum bits required as well as the computational complexity. Then, this paper analyzes, compares and summarizes the similarities and differences between these quantum image representations. Finally, the challenges and future development in the field of quantum image processing are summarized and forecasted. This work will help researchers to understand the advances related to quantum image representations in the field of quantum image processing and is therefore of some reference value.

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Section: R Qmcrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Trend of Quantum Image Representations

Guo

Liu

et al. 2020

IEEE Access

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“…For the novel enhanced quantum representation (NEQR) of digital images [5], Abdolmaleky [6] proposed a quantum representation model for RGB images, and the color information was represented by three channels of the red, green and blue. Similar to the novel quantum image representation method, this model uses two quantum entangled sequences to represent the color and the position of pixels.…”

Section: Quantum Image Representationmentioning

confidence: 99%

“…Therefore, quantum color image steganography with high capacity, high fidelity, and strong concealment is needed urgently. This study used quantum RGB image models [6] to represent color images and divided quantum color images into blocks. The gray level difference of the pixel G channel in the blocks was calculated.…”

Section: Introductionmentioning

confidence: 99%

Steganography of Quantum Color Images Based on Blocking and Gray Level Difference

Luo¹,

Shi²,

Oad³

et al. 2020

JESTR

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Steganography of quantum images is restricted by high fidelity and robustness, which limit hiding capacity and decrease visual quality. To improve the concealment of steganographic embedding of secret messages and enhance embedding capacity, the present study proposed a quantum red-green-blue (RGB) image steganography based on image block and gray level difference. First, digital color images were represented by quantum models and divided into non-overlapping blocks. Second, reference pixels in the blocks were selected, and gray level difference between the reference pixels and the G channel of other pixels was calculated. Third, according to the location of the difference, different bit planes where secret messages were embedded into the R and the B channels of the carrier image were determined. Finally, steganographic image was divided into blocks. The G channel gray difference was calculated, and the original secret message was extracted. Results demonstrate that quantum RGB image steganography has a steganographic capacity of four bits per pixel, and its quantum circuit is less complex. In comparison with other quantum image steganographic methods, the proposed method ensures a large steganographic capacity while realizing satisfactory visual quality and steganographic concealment. The study provides reference significance for exploring data hiding and quantum image security based on quantum RGB image block.

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“…Cada canal possui um valor que varia de 0 a 255. Assim, cada cor possuirá um valor de R, um valor de G e um valor de B (ABDOLMALEKY et al, 2017).…”

Section: Introductionunclassified

Uso do aplicativo PhotoMetrix no monitoramento da concentração de flúor em sistemas alternativos de abastecimento de água

Baumann¹,

Librelotto²,

Pappis³

et al. 2019

R. Águas Subter.

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A ingestão frequente de água contendo concentração de flúor acima do limite estabelecido pela legislação brasileira causa uma doença chamada fluorose dentária. Atualmente, o monitoramento do nível de flúor na água para consumo humano é realizado utilizando métodos potenciométricos, cromatográficos ou colorimétricos, que são relativamente caros e utilizam equipamentos de difícil deslocamento para análises de campo. Portanto, surge a necessidade de desenvolver métodos alternativos, visando rapidez, praticidade, mobilidade e redução do custo das análises. O aplicativo para smartphone PhotoMetrix® é um software livre de análise de imagens digitais utilizando modelos matemáticos univariados e multivariados. A captura de imagens pode ser feita através do próprio smartphone ou com uma câmera USB externa. Assim, o objetivo deste estudo foi avaliar o uso do PhotoMetrix® no monitoramento da concentração de flúor em águas de abastecimento. Dois métodos de referência foram utilizados para comparação dos resultados: o método espectrofotométrico e o eletrodo de íon seletivos. Os resultados indicaram concordância entre os métodos, mostrando um coeficiente de determinação (R2) de 0,995 e erro de predição RMSEP (Root Mean Square Error of Prediction) de aproximadamente 0,05 mg L-1. Então, o PhotoMetrix® é um método satisfatório para a determinação de flúor, podendo se tornar uma alternativa viável para o monitoramento da qualidade da água, especialmente para sistemas alternativos de abastecimento.

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Red-Green-Blue multi-channel quantum representation of digital images

Cited by 101 publications

References 30 publications

A New Trend of Quantum Image Representations

A New Trend of Quantum Image Representations

Steganography of Quantum Color Images Based on Blocking and Gray Level Difference

Uso do aplicativo PhotoMetrix no monitoramento da concentração de flúor em sistemas alternativos de abastecimento de água

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