Discussion on introduction of quantum information and computational science in the image data compression procedure of 360 degrees camera

Quantum computers are known to efficiently solve problems that remain unsolved by classical von Neumann-type computers. However, in the research and development phase within the field of information science, quantum information theory is prioritized, and the extent of information processing achievable at the experimental stage is generally not well understood. In particular, data compression is a key concept and challenge in image processing and multimedia processing within quantum information processing. A prime example of data compression in quantum computing is the quantum Fourier transform. With the advent of experimental environments enabling simplified simulations using quantum simulators developed by Google and IBM, we considered that verification might now be possible. Still images captured by 360 degree cameras are full-sky images, requiring pixel counts equivalent to 4K to 8K. This results in significantly larger data volumes compared to standard high-definition still images. Therefore, when encoding these images, it is desirable to efficiently reduce the information volume while preserving quality as much as possible. We considered whether insights from quantum information computing science could be applied to achieve this. Therefore, in this study, we experimentally investigated the effectiveness, usefulness, and potential of quantum information science for image compression by simulating the quantum Fourier transform using Qiskit during the data compression process of 360 degree camera images.