ABSTRACT Achieving reliable high-fidelity image transmission over dynamic channels remains a critical challenge in modern communication systems, particularly in vehicular environments with significant noise impairments. In response, this paper proposes a novel quantum multiple description coding (Q-MDC) framework to enhance image transmission robustness and reconstruction quality. The proposed approach partitions the source image at the pixel level into odd and even components to generate two primary descriptions, which are subsequently converted into bitstreams using various source encoding techniques, including JPEG, JPEG2000, HEIF, and uncompressed formats. These bitstreams are optionally channel encoded and encoded into quantum states in the computational basis. A third correlated description is generated using a controlled-NOT operation, embedding inter-description redundancy while maintaining equal bandwidth across all descriptions. Each description is transformed into quantum superposition states using a Hadamard-based encoder and transmitted over independent noisy channels. At the receiver, inverse Hadamard operations restore the computational basis representation. A packet-wise and qubit-wise deterministic recovery mechanism is then employed to reconstruct corrupted descriptions by exploiting inter-description dependencies. The recovered descriptions are measured, followed by channel decoding and source decoding to reconstruct the image. Simulation results demonstrate that the proposed Q-MDC framework significantly improves peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI) compared to classical MDC and channel-coded single-stream transmission schemes, achieving SNR gains of up to 4 dB and 7 dB, respectively. The results highlight the effectiveness of the proposed approach for robust multimedia transmission in error-prone wireless environments.INDEX TERMS Image Transmission, Multiple Description Coding (MDC), Noise Resilience, Quantum Communication, Quantum Superposition
ABSTRACT Achieving reliable high-fidelity image transmission over dynamic channels remains a critical challenge in modern communication systems, particularly in vehicular environments with significant noise impairments. In response, this paper proposes a novel quantum multiple description coding (Q-MDC) framework to enhance image transmission robustness and reconstruction quality. The proposed approach partitions the source image at the pixel level into odd and even components to generate two primary descriptions, which are subsequently converted into bitstreams using various source encoding techniques, including JPEG, JPEG2000, HEIF, and uncompressed formats. These bitstreams are optionally channel encoded and encoded into quantum states in the computational basis. A third correlated description is generated using a controlled-NOT operation, embedding inter-description redundancy while maintaining equal bandwidth across all descriptions. Each description is transformed into quantum superposition states using a Hadamard-based encoder and transmitted over independent noisy channels. At the receiver, inverse Hadamard operations restore the computational basis representation. A packet-wise and qubit-wise deterministic recovery mechanism is then employed to reconstruct corrupted descriptions by exploiting inter-description dependencies. The recovered descriptions are measured, followed by channel decoding and source decoding to reconstruct the image. Simulation results demonstrate that the proposed Q-MDC framework significantly improves peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI) compared to classical MDC and channel-coded single-stream transmission schemes, achieving SNR gains of up to 4 dB and 7 dB, respectively. The results highlight the effectiveness of the proposed approach for robust multimedia transmission in error-prone wireless environments.INDEX TERMS Image Transmission, Multiple Description Coding (MDC), Noise Resilience, Quantum Communication, Quantum Superposition
Achieving reliable high-fidelity image transmission over dynamic channels remains a critical challenge in modern communication systems, particularly in vehicular environments with significant noise impairments. In response, this paper proposes a novel quantum multiple description coding (Q-MDC) framework to enhance image transmission robustness and reconstruction quality. The proposed approach partitions the source image at the pixel level into odd and even components to generate two primary descriptions, which are subsequently converted into bitstreams using various source encoding techniques, including JPEG, JPEG2000, HEIF, and uncompressed formats. These bitstreams are optionally channel encoded and encoded into quantum states in the computational basis. A third correlated description is generated using a controlled-NOT operation, embedding inter-description redundancy while maintaining equal bandwidth across all descriptions. Each description is transformed into quantum superposition states using a Hadamard-based encoder and transmitted over independent noisy channels. At the receiver, inverse Hadamard operations restore the computational basis representation. A packet-wise and qubit-wise deterministic recovery mechanism is then employed to reconstruct corrupted descriptions by exploiting inter-description dependencies. The recovered descriptions are measured, followed by channel decoding and source decoding to reconstruct the image. Simulation results demonstrate that the proposed Q-MDC framework significantly improves peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI) compared to classical MDC and channel-coded single-stream transmission schemes, achieving SNR gains of up to 4 dB and 7 dB, respectively. The results highlight the effectiveness of the proposed approach for robust multimedia transmission in error-prone wireless environments.
Reliable transmission of compressed images over error-prone channels remains challenging because compressed bitstreams are highly sensitive to channel impairments. Although existing quantum-inspired encoding schemes exploit superposition to improve transmission reliability, they remain vulnerable to channel noise, limiting reconstructed image quality. To address these limitations, this paper proposes a chirp transform-based multi-qubit encoding framework for robust compressed image transmission. Unlike conventional transform-based approaches, including the Hadamard transform and Quantum Fourier Transform (QFT), the proposed framework introduces nonlinear (quadratic) phase modulation into the multi-qubit representation, producing a high-dimensional superposition that enhances noise resilience through improved phase separability. This nonlinear phase variation improves error resilience through phase-induced decorrelation, reducing noise-induced distortions. The system integrates Joint Photographic Experts Group (JPEG) and High Efficiency Image File Format (HEIF) source encoding, followed by channel coding, multi-qubit encoding, and chirp transformation before transmission through a simulated quantum channel. At the receiver, inverse chirp transformation, measurement-based decoding, channel decoding, and source decoding reconstruct the transmitted image. Performance is evaluated using Bit Error Rate (BER), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Quality Index (UQI). Experimental results demonstrate that the proposed framework achieves up to a 3 dB signal-to-noise ratio (SNR) gain over QFT-based and Hadamard-based systems in the single-qubit configuration. For the eight-qubit configuration, gains of up to 2 dB over QFT-based systems and 7 dB over Hadamard-based systems are achieved. These results demonstrate the effectiveness and scalability of the proposed framework for robust compressed image transmission over noisy quantum communication channels.
Reliable transmission of compressed images over error-prone channels remains challenging because compressed bitstreams are highly sensitive to channel impairments. Although existing quantum-inspired encoding schemes exploit superposition to improve transmission reliability, they remain vulnerable to channel noise, limiting reconstructed image quality. To address these limitations, this paper proposes a chirp transform-based multi-qubit encoding framework for robust compressed image transmission. Unlike conventional transform-based approaches, including the Hadamard transform and Quantum Fourier Transform (QFT), the proposed framework introduces nonlinear (quadratic) phase modulation into the multi-qubit representation, producing a high-dimensional superposition that enhances noise resilience through improved phase separability. This nonlinear phase variation improves error resilience through phase-induced decorrelation, reducing noise-induced distortions. The system integrates Joint Photographic Experts Group (JPEG) and High Efficiency Image File Format (HEIF) source encoding, followed by channel coding, multi-qubit encoding, and chirp transformation before transmission through a simulated quantum channel. At the receiver, inverse chirp transformation, measurement-based decoding, channel decoding, and source decoding reconstruct the transmitted image. Performance is evaluated using Bit Error Rate (BER), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Quality Index (UQI). Experimental results demonstrate that the proposed framework achieves up to a 3 dB signal-to-noise ratio (SNR) gain over QFT-based and Hadamard-based systems in the single-qubit configuration. For the eight-qubit configuration, gains of up to 2 dB over QFT-based systems and 7 dB over Hadamard-based systems are achieved. These results demonstrate the effectiveness and scalability of the proposed framework for robust compressed image transmission over noisy quantum communication channels.
Multi-qubit encoding is a quantum encoding technique that uses multiple qubits to represent information, enabling higher data throughput. This study explores its application in compressed image transmission, comparing its performance to single-qubit encoding in both uncoded and channel-coded configurations. For image compression, JPEG and HEIF formats are used, with source coding rates adjusted to ensure similar bandwidth utilization. The compressed bitstream is optionally protected by channel coding before undergoing quantum encoding using qubit sizes from 1 to 8 for quantum channel transmission. At the receiver, quantum decoding reconstructs classical bits, followed by optional channel decoding, and finally, source decoding restores the image. Image quality is assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and universal quality index (UQI). Results show that increasing the qubit encoding size improves noise resilience, with eight-qubit encoding achieving a maximum channel SNR gain of 15 dB for JPEG and 17 dB for HEIF under channel coding, demonstrating greater robustness compared to single-qubit encoding but with higher computational complexity. To address this, an adaptive multi-qubit encoding process is proposed, dynamically adjusting qubit usage by utilizing fewer qubits for lower noise levels and higher qubits for higher noise conditions while balancing noise resilience and computational complexity under the assumption of perfect channel estimation.
Quantum image transmission is a critical enabler for next-generation communication systems, allowing for the reliable exchange of high-quality visual data over error-prone quantum channels. Existing quantum-encoding schemes, however, often suffer from limited efficiency and reduced robustness under noisy conditions. This work introduces a novel multi-qubit phase-shift keying (PSK) encoding framework to enhance both fidelity and reliability in quantum image transmission. In the proposed system, source-encoded images (JPEG/HEIF) are converted into bitstreams, segmented into varying qubit sizes from 1 to 8, and mapped onto multi-qubit states using quantum PSK modulation. By exploiting multi-qubit superposition and phase modulation, the scheme improves spectral efficiency while maintaining resilience to channel noise. The encoded quantum states are transmitted through noisy channels and reconstructed via inverse quantum operations combined with classical post-processing to recover the original images. Experimental results demonstrate substantial performance improvements, evaluated using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI). Compared to superposition-only approaches, the proposed method achieves up to 3 dB SNR gain for higher qubit sizes, while single-qubit encoding remains limited due to reduced phase utilization. Moreover, relative to classical communication systems, the proposed multi-qubit PSK scheme consistently outperforms across all tested qubit sizes, highlighting its effectiveness for reliable, efficient, and high-fidelity quantum image transmission.
Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems.