Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Theoretical Physics

Quantum Image Histogram Statistics

Nan Jiang, Zhuoxiao Ji, Jian Wang, Xiao-Ming Lu, Ri‐Gui Zhou

Published Oct 8, 2020• 6 citations
Publisher
DOI
10.1007/s10773-020-04614-x

Related papers

Histogram-based segmentation of quantum images

Simona Caraiman, Vasile Manta

Theoretical Computer Science•Aug 14, 2013•89 citations

Order-encoded quantum image model and parallel histogram specification

Guanlei Xu, Xiaogang Xu, Xun Wang +1 more

Quantum Information Processing•Oct 3, 2019•32 citations

A new general model for quantum image histogram (QIH)

Shahrokh Heidari, M.M. Abutalib, Majid Alkhambashi +2 more

Quantum Information Processing•Apr 25, 2019•25 citations

A Quantum Algorithm Of Constructing Image Histogram

Yi Zhang, Kai Jian Lu, Yinghui Gao +1 more

Histogram plays an important statistical role in digital image processing. However, the existing quantum image models are deficient to do this kind of image statistical processing because different gray scales are not distinguishable. In this paper, a novel quantum image representation model is proposed firstly in which the pixels with different gray scales can be distinguished and operated simultaneously. Based on the new model, a fast quantum algorithm of constructing histogram for quantum image is designed. Performance comparison reveals that the new quantum algorithm could achieve an approximately quadratic speedup than the classical counterpart. The proposed quantum model and algorithm have significant meanings for the future researches of quantum image processing.

•May 23, 2013•7 citations

Quantum Image Processing

Fei Yan, Salvador Elías Venegas-Andraca

Springer eBooks•Jan 1, 2020•70 citations

An improved order-encoded quantum image representation model and its application

Kai Xu, Shukun Ran

Quantum image representation is an important research direction of quantum image processing, which mainly studies the use of quantum properties to encode and store images. At present, most quantum image representation models store image grayscale-related information, but lack histogram-related information. A few quantum representation models that store image histogram information cannot accurately retrieve the image, resulting in the difficulty of quantum image histogram processing. Therefore, this paper proposes an improved order-encoded quantum image representation model ONEQR, which uses the quantum basis state sequence to store the grayscale information, real position information and ascending position information of the image, which can not only solve the problem of inability to accurately retrieve the image, but also store the histogram information of the image, which is convenient for the implementation of the histogram processing algorithm of quantum image; In addition, based on ONEQR, a quantum image representation model MULTI-ONEQR that stores multiple images is proposed, and based on this model, a quantum image histogram specification (HS) algorithm is proposed, which can use quantum properties to complete image HS in parallel. Finally, the simulation experiment of the HS algorithm based on the ONEQR model is carried out to verify the effectiveness of the algorithm.

International Journal of Quantum Information•Mar 17, 2023•1 citation
Histogram plays an important statistical role in digital image processing. However, the existing quantum image models are deficient to do this kind of image statistical processing because different gray scales are not distinguishable. In this paper, a novel quantum image representation model is proposed firstly in which the pixels with different gray scales can be distinguished and operated simultaneously. Based on the new model, a fast quantum algorithm of constructing histogram for quantum image is designed. Performance comparison reveals that the new quantum algorithm could achieve an approximately quadratic speedup than the classical counterpart. The proposed quantum model and algorithm have significant meanings for the future researches of quantum image processing.
Quantum image representation is an important research direction of quantum image processing, which mainly studies the use of quantum properties to encode and store images. At present, most quantum image representation models store image grayscale-related information, but lack histogram-related information. A few quantum representation models that store image histogram information cannot accurately retrieve the image, resulting in the difficulty of quantum image histogram processing. Therefore, this paper proposes an improved order-encoded quantum image representation model ONEQR, which uses the quantum basis state sequence to store the grayscale information, real position information and ascending position information of the image, which can not only solve the problem of inability to accurately retrieve the image, but also store the histogram information of the image, which is convenient for the implementation of the histogram processing algorithm of quantum image; In addition, based on ONEQR, a quantum image representation model MULTI-ONEQR that stores multiple images is proposed, and based on this model, a quantum image histogram specification (HS) algorithm is proposed, which can use quantum properties to complete image HS in parallel. Finally, the simulation experiment of the HS algorithm based on the ONEQR model is carried out to verify the effectiveness of the algorithm.