| Popis: |
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. In this paper, an autoencoder based image quality metric is proposed 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, semantically received image is inserted to the same autoencoder to generate the corresponding latent vector space. Finally, both vector spaces are used to define the Euclidian 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 89.1% 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 and SSIM. |
