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.