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.

Quantum communication is expected to become the cornerstone of global communication systems, addressing critical issues of classical communication while providing unprecedented security and efficiency. A crucial aspect of advancing this field is quantum channel coding, which ensures data integrity by detecting and correcting errors specific to quantum systems. This research evaluates the performance of the three-qubit error correction code, the fundamental and simplest technique in quantum channel coding, for image transmission over error-prone channels. JPEG and HEIF format images are encoded using the three-qubit error correction method and compared to the 1/3 rate polar codes. Our results demonstrate that the three-qubit error correction code significantly outperforms advanced classical polar codes in both classical and quantum domains, achieving a maximum PSNR of 61.5 dB (SSIM = 0.9997) in HEIF and 58.3 dB (SSIM = 0.9994) in JPEG. This showcases its potential as a robust solution for quantum communication.

Introductions 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 increase of 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 in 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.

Semantic communication has attracted significant attention as a key technology for emerging 6G communications. Though it has lots of potentials specially for high volume media communications, still there is no proper quality metric for modelling the semantic noise in semantic communications. This paper proposes an autoencoder based image quality metric to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder decoder model and calculate its latent vector space. Once it is trained, a semantically generated/received image is inserted to the same autoencoder to create the corresponding latent vector space. Finally, both vector spaces are used to define the Euclidean space between two spaces to calculate the Mean Square Error between two vector spaces, which is used to measure the effectiveness of the semantically generated image. Results indicate that the proposed model has a correlation coefficient of 88% with the subjective quality assessment. Furthermore, the proposed model is tested as a metric to evaluate the image quality in conventional image coding. Results indicate that the proposed model can also be used to replace conventional image quality metrics such as PSNR,SSIM,MSSIM,UQI, VIFP, and SSC whereas these conventional metrics completely failed in semantic noise modelling.