Autoencoder based image quality metric for modelling semantic noise in semantic communications
Samarathunga, Prabhath and Fernando, Thanuj and Gowrisetty, Vishnu and Atulugama, Thisarani and Fernando, Anil (2024) Autoencoder based image quality metric for modelling semantic noise in semantic communications. Electronics Letters, 60 (4). e13115. ISSN 0013-5194 (https://doi.org/10.1049/ell2.13115)
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Abstract
Semantic communication has attracted significant attention as a key technology for emerging 6G 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 (LVS) and then a semantically generated/received image is inserted into the same autoencoder to create the corresponding LVS. Finally, both LVS are used to define the Euclidean space to calculate the mean square error between two LVS. Results indicate that the proposed model has a high correlation coefficient of 88% with subjective quality assessment and commonly used conventional metrics completely failed in semantic noise modelling.
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Item type: Article ID code: 88225 Dates: DateEvent15 February 2024Published15 February 2024Published Online25 January 2024AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering > Telecommunication Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 19 Feb 2024 16:10 Last modified: 28 Nov 2024 01:27 URI: https://strathprints.strath.ac.uk/id/eprint/88225