Adaptive coding based quantum communication system for image
transmission over error-prone channels
Udara Jayasinghe, Prabhath Samarathunga, Yasith Ganearachchi, Thanuj
Fernando, and Anil Fernando
Adaptive coding in quantum communication offers a promising approach to
enhance the efficiency and reliability of data transmission using
quantum superposition, particularly in noisy and error-prone channels.
This study investigates the effectiveness of quantum communication
combined with adaptive coding for compressed image transmission using
Joint Photographic Experts Group (JPEG) codec and High Efficiency Image
Format (HEIF) with polar codes at varying rates. To maintain a similar
bandwidth, the source coding rates are adjusted according to the channel
coding rates. The results show that the adaptive coding based quantum
communication system significantly outperforms equivalent classical
systems, especially at low signal-to-noise ratios (SNR), achieving Peak
Signal-to-Noise Ratio (PSNR) improvements up to 65 dB and Structural
Similarity Index Measure (SSIM) values up to 0.9999 for HEIF images and
PSNR values up to 58 dB with SSIM values up to 0.9994 for JPEG images.
These findings demonstrate the superior robustness and higher image
quality of adaptive coding based quantum communication in varying
channel noise conditions and bandwidth-restricted applications.
Introduction: Quantum communication is a groundbreaking
technology that leverages quantum properties such as superposition
[1] and entanglement [2] to overcome limitations of classical
communication such as channel capacity and noise distortion, enabling
more efficient and reliable data transmission. Image transmission is an
increasingly important area where reliable transmission over
capacity-limited and noisy channels has become a necessity, with many
remote sensing applications which operate in such conditions requiring
image capture and transmission. Classical communication systems
typically experience drastic performance degradation under such noisy
conditions, resulting in significant drops in received image quality.
Due to the exploitation of pixel correlation in image compression
schemes, such as the Joint Photographic Experts Group (JPEG) codec and
the High Efficiency Image Format (HEIF), which are used for efficient
image transmission, they are highly susceptible to errors caused by
channel noise.
Channel coding schemes, which add redundancy to the compressed
bitstream, are employed to overcome this challenge. These schemes enable
detection and correction of bitstream errors up to an extent but come at
the cost of adding complexity and increasing the required bandwidth.
Common channel coding schemes include low density parity check (LDPC)
codes, turbo codes, and polar codes. However, polar codes [3] are
widely used in modern classical communication systems due to their
efficiency in coding and superior error tolerance compared to other
classical channel coding methods.
However, since most communication channels are time-variant, channel
conditions change with time and thus create variance in the noise that
affects signals transmitted through the channel. In such a situation,
the use of a constant channel coding rate is neither efficient nor
effective. Adaptive coding techniques [4] have emerged as effective
solutions to optimise the system performance under such conditions. By
dynamically adjusting the coding rate in response to real-time channel
conditions, adaptive coding improves data transmission efficiency,
ensuring optimal utilisation of available bandwidth. Multiple studies
have explored adaptive coding in the realm of classical communication
systems [5], [6], [7], but research on its application in
quantum communication remains limited [8].
Notably, there is a significant gap in studies focusing on adaptive
coding for end-to-end quantum communication, specifically aimed at image
transmission. While a few studies have explored media transmission using
quantum communication with quantum entanglement [9], [10], and
quantum communication with quantum superposition, which has been shown
to be highly effective for high-quality and reliable media transmission
[11], none have utilised adaptive coding in this domain. This
underscores the need for further exploration, as adaptive coding has the
potential to significantly enhance the performance of quantum
communication systems, especially in error-prone environments.
Therefore, this study investigates the performance of adaptive coding
for image transmission in quantum communication with quantum
superposition, specifically focusing on JPEG and HEIF image compression
schemes. We use polar codes to implement an adaptive channel coding
method, employing varying code rates to assess their impact on
transmission quality. By examining the effects of channel noise on image
transmission, we demonstrate the superior capabilities of the proposed
quantum communication based model over the equivalent classical
communication based model when combined with adaptive coding strategies.
The findings highlight the potential of this innovative approach to meet
the growing demand for bandwidth in media transmission while providing
efficient, robust, and reliable solutions to the challenges posed by
real-time channel noise in modern communication systems.