The introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth‐intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication‐based image transmission system indicates the potential of the approach to be applied to satisfy the ever‐increasing demands of high‐quality media transmission applications.

Quantum communication offers transformative potential for media transmission by addressing the limitations of classical communication systems. To realize this potential, the study proposes a quantum communication framework for transmitting compressed videos over error-prone channels, leveraging quantum superposition. Two channel coding schemes are analyzed: quantum error correction (three-qubit, five-qubit, and seven-qubit codes) and classical error correction (1/3 rate polar code), all operating within the same bandwidth constraints. The proposed systems are benchmarked against a classical communication system using 1/3 rate polar codes. Results show that the three-qubit error correction-based quantum communication system, while simple and efficient, achieves significant performance gains over both classical error correction-based quantum and classical communication systems, with up to 41.42 dB in peak signal-to-noise ratio (PSNR), 0.9639 in structural similarity index measure (SSIM), and 94.4042 in video multimethod assessment fusion (VMAF). However, the five-qubit and seven-qubit systems outperform the three-qubit system, with the seven-qubit system surpassing all others in high noise environments, demonstrating its robustness across various group of pictures (GOP) formats. These findings highlight the trade-offs between simplicity and complexity, as the three-qubit system is practical and efficient, while the five-qubit and seven-qubit channel codes offer higher fidelity and resilience at the cost of increased complexity.

Requirements of next-generation video applications are becoming a challenge for conventional video coding systems, although they have evolved over decades to accommodate the most demanding of current video applications. Semantic communications, built on the concept of transmitting just the semantics of a message and allowing the receiver to reconstruct the message based on a shared context, is a non-conventional approach being considered to overcome these challenges and improve performance of video coding systems. In this paper, a first such semantic communication-based video coding system in hybrid mode is proposed, which uses an autoencoder-based semantic encoder for inter coding, augmented by the intra coding capabilities of Versatile Video Coding (VVC) to encode key frames that form the context for the semantic communication and the residuals for improving the fidelity of the output frames. For a range of videos with differing levels of complexity, the proposed system consistently outperforms High Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC) in terms of rate distortion metrics quantified by Bjontegaard Delta Rates. It also outperforms Versatile Video Coding with videos with low or high complexity, but slightly falls behind with videos with medium complexity, which can be improved by addressing the open research areas that stem from this work. The proposed system demonstrates the potential of semantic communication based video coding systems to consistently outperform state-of-the-art conventional video coding systems over a wide range video applications.

Quantum communication systems have the potential to revolutionize media transmission technologies with unparalleled efficiency and reliability. However, practical and scalable implementations are challenged by issues such as channel fading and interference. To address these, we propose a novel approach combining quantum superposition with transmit and receive diversity schemes in Multiple-Input Multiple-Output (MIMO) systems, designed to mitigate the effects of fading for enhanced image transmission. In our simulations, images in JPEG and HEIF formats are channel encoded with rate 1/2 polar coding, converted into qubit superposition states, and transmitted through a 2x2 MIMO system with varied diversity schemes. At the receiver, a quantum decoder reconstructs the classical information, followed by polar decoding to retrieve the original image data. Our approach achieves notable improvements in image quality, with Peak Signal-to-Noise Ratio (PSNR) up to 58.27 dB for JPEG and 64.72 dB for HEIF, and Structural Similarity Index Measure (SSIM) up to 0.9994 for JPEG and 0.9999 for HEIF, outperforming classical systems, especially under low Signal-to-Noise Ratio (SNR) conditions, demonstrating the system’s enhanced ability to maintain image quality in noisy channels. These findings highlight the promise of quantum superposition-based media transmission to set new standards in reliable, high-fidelity communication for next-generation systems.

As wireless networks approach their Shannon capacity limit amid exponentially increasing data demands, semantic communication, which transmits the meaning of data rather than its exact form, has emerged as a compelling alternative. However, a key challenge lies in effectively modeling and mitigating both semantic and technical noise in such systems. We analyze a framework for semantic image transmission that independently and jointly models these two types of noise while employing Turbo codes for robust error detection and correction. We systematically examine their individual and combined impacts through seven experimental scenarios and benchmark the results against state-of-the-art image codecs. Our findings reveal that joint noise modeling not only improves semantic fidelity, but also enhances overall system robustness. Turbo coding proves effective in mitigating both noise types, enabling accurate meaning preservation even under adverse channel conditions. The simulation results demonstrate a reduction of approximately 33% in the channel bit rate when using joint modeling compared to alternative approaches, without compromising visual quality. Comparative evaluations against conventional codecs such as JPEG, JPEG2000, HEIF, and Turbo-coded image transmission systems highlight the superior performance of our semantic-aware strategy in noisy, bandwidth-constrained environments. This research underscores the value of integrating semantic intelligence with classical coding techniques, paving the way for next-generation wireless communication systems that are more efficient, resilient, and customized for emerging applications such as 6G, autonomous systems, and AI-enhanced networks.

Quantum communication offers unparalleled reliability and efficiency, making it a promising solution for high‐quality media transmission. To explore this potential, we propose a quantum communication system for image transmission, addressing the challenges of transmitting high‐quality image data over error‐prone channels. A crucial aspect of advancing this field is quantum channel coding, specific to quantum systems. Therefore, this research evaluates the performance of low‐complex three‐qubit quantum error correction code for image transmission over noisy channels. JPEG and HEIF images are encoded using three‐qubit error correction method and compared to 1/3 rate polar codes with equivalent bandwidth. Results show that the three‐qubit error correction code significantly outperforms advanced classical polar codes in both classical and quantum domains, achieving a maximum PSNR of 64.5 dB (SSIM = 0.9997) in HEIF and 58.3 dB (SSIM = 0.9994) in JPEG. These findings underscore its potential as a robust solution for quantum communication in media transmission.