Abstract:
Quantum image representations are the models that are used to represent digital images on to the quantum computers. They also allow to perform various
image processing operations on these images and to store on to the quantum
system. For storing images on to the quantum computers, QIR models use
qubits. The FRQI and NEQR are well-known models used for capturing and
processing quantum images. But these models have some weaknesses especially they suffer from time and space complexity respectively. Therefore, in
this research, we establish that the complexity of image preparation in FRQI
model is O(n2
2n
), which is linear in the size of image. Moreover, by analyzing
the FRQI and NEQR models, we propose an improved flexible representation of quantum images (IFRQI) which takes p qubits to encode gray-scale
values of pixels of a 2p-bit-deep image. The gray-scale values are encoded by
employing rotation matrices corresponding to chosen values of angles which
assist in accurate retrieval of original image information through projective
measurements. The quantum image compression algorithm and basic image
processing operations are discussed in detail to establish the effectiveness of
IFRQI model. The performance analysis in respect of time and space complexity exhibits that the IFRQI model is comparable to FRQI and NEQR
models.
