META-LEARNING-BASED JOINT SOURCE-CHANNEL CODING METHOD AND APPARATUS, AND MEDIUM

Original (Chinese)

Provided are a meta-learning-based joint source-channel coding (JSCC) method and apparatus, and a medium. The method includes: obtaining a target image; performing JSCC on the target image through a preset target model to obtain a coding result; and transmitting the target image based on the coding result. The target model is obtained by performing inner-loop and outer-loop training on a preset neural network model based on a plurality of meta-learning tasks. The plurality of meta-learning tasks are constructed based on different average channel signal-to-noise ratios (SNRs). In the meta-learning-based JSCC method and apparatus, and the medium, JSCC is performed on the target image through the target model with excellent channel environment adaptability and image coding and transmission capabilities, to obtain the coding result for transmitting the target image. This can resolve a problem that effective image transmission is difficult under different channel conditions in few-shot scenarios.