Advance quantum image representation and compression using DCTEFRQI approach

TL;DR

This paper introduces DCTEFRQI, a quantum image representation and compression method combining DCT and EFRQI, which improves efficiency and quality over existing quantum image processing techniques.

Contribution

The paper proposes a novel quantum image compression scheme using DCT and EFRQI, demonstrating improved performance in representation and compression quality.

Findings

DCTEFRQI outperforms DCT-GQIR, DWT-GQIR, and DWT-EFRQI in PSNR and bit rate.

Uses 16 qubits to encode gray images efficiently.

Simulation results validate the effectiveness of the proposed method.

Abstract

In recent year, quantum image processing got a lot of attention in the field of image processing due to opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimension to locate and process the image data faster. Moreover, several researches show that, the computational time of quantum process is faster than classical computer. By encoding and compressing the image in quantum domain is still challenging issue. From literature survey, we have proposed a DCTEFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. The objective of this work is to represent and compress various gray image size in quantum computer using DCT(Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approach together. Quirk simulation tool is used to design corresponding quantum image circuit. Due to limitation of qubit, total 16 numbers of qubit are used to represent the gray scale image among those 8 are used to map the coefficient values and the rest 8 are used to generate the corresponding coefficient position. Theoretical analysis and experimental result show that, proposed DCTEFRQI scheme provides better representation and compression compare to DCT-GQIR, DWT-GQIR and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio) and bit rate..