Research On Expression Reconstruction Algorithms For Semantic Communications

The development of modern communication technology has been rapid,and semantic communication has received increasing attention and research.With the continuous maturity of deep learning technology,semantic communication can be implemented more accurately and efficiently.Facial expressions can convey rich emotions and intentions,helping people better understand each other’s states and intentions.Intentional communication can be better achieved through facial expression reconstruction.Against this background,the facial expression reconstruction algorithm for semantic communication studied in this paper has important research significance and value.Aiming at the actual transmission scenarios of high bit rate and low transmission bandwidth,this paper proposes an expression reconstruction algorithm for semantic communication.The algorithm is based on denoising autoencoder and is mainly applied to unoccluded expression semantic scenes.The algorithm design includes coding layer,channel layer and decoding layer,and introduces attention mechanism and visual converter to enhance the learning ability of the network model on global features.Compared with the traditional high-order modulation 256QAM under AWGN channel conditions,the proposed algorithm can achieve better expression reconstruction effect of real data sets under low signal-to-noise ratio under the condition of AWGN channel,and also has good performance in quantitative evaluation indicators.Under the condition of Rayleigh channel,the algorithm can obtain reconstruction results better than 256QAM modulation under the conditions of low signal-to-noise ratio and high signal-to-noise ratio,and has obvious advantages in quantitative evaluation indicators.This paper proposes a facial expression reconstruction algorithm to address the problem of missing facial expression semantics caused by the occlusion of the facial expression semantic part.Based on the denoising diffusion probability model,this algorithm introduces attention mechanism,residual block,and affine transformation to enhance the performance of the network model.The algorithm can enhance the reconstructed semantic information of the unoccluded facial expressions and improve the model’s understanding of the original facial expressions by adjusting parameters such as the number of noise injection rounds,denoising rounds,sampling distance,and sampling frequency.The algorithm proposed in this paper is compared with existing methods for recovering facial expression semantics from occluded images through experimental testing.The results show that the proposed algorithm has better qualitative reconstruction effects and also performs well in quantitative indicators.By investigating the above two points,this paper has achieved facial expression reconstruction for both unoccluded and occluded semantic communication scenarios,with good results in both qualitative and quantitative analysis.