Abstract
Due to the rapid development of satellite imaging sensors,
high-resolution images are being generated for use. Various image
processing algorithms, such as deep learning models, require images of
reduced sizes given the computational constraints. Hence, preprocessing
the images to reduce their size is crucial for any deep learning model.
This paper proposes a novel approach to compress satellite images using
quantum computing. A comparative study on different standard data
embedding techniques used in quantum computing is undertaken. We propose
four quantum compression techniques (ππΆπs) by extending the unitary
operations of amplitude encoding for compressing satellite images. The
proposed methods provide exponential scaling as amplitude encoding is
used, where 2π classical data values are encoded into
π qubits. Compression performance, visual evaluation, and quality metric
evaluation were carried out to assess the proposed compression
techniques. Our experimental results showed that the crucial patterns in
images are retained in the compressed images without quality loss even
after 75% compression. The compressed images can be used for
post-processing tasks such as classification using classical or quantum
computing algorithms.