## 2 Proposed Algorithm
### The Generators Incorporate Lightweight Attention Mechanisms  
The objective of optimizing video transmission is to facilitate the reception of a superior visual image. Although CycleGAN has demonstrated the capacity to reproduce image details with greater precision, this article proposes the integration of a lightweight attentional mechanism within the residual layer of the GAN generator as a means of enhancing the perceptual quality perceived by the human eye. The network structure is illustrated in Figure 5.  
The attention mechanism comprises two distinct components. The first part is to enhance the feature processing based on the spatial distribution of its own pixels after normalized feature extraction of the single-frame image; the second part is to compensate for the spatio-temporal action information by combining the images before and after the video frame in which the image is located, after channel pooling. Firstly, single-frame images are batch normalized after maximum pooling as well as average pooling. This process transforms the input data at each level into a distribution with the same mean and variance, typically a standard normal distribution (mean 0, variance 1). This approach has been shown to accelerate training, enhance model convergence, and diminish the model's sensitivity to initialization and learning rate choices while simultaneously reducing internal covariate bias. Subsequently, the image is subjected to a ReLU (rectified linear unit) function, which is a prevalent activation function, particularly in convolutional neural networks within the domain of deep learning. In such contexts, the primary function of the activation function is to introduce non-linearities, thereby enabling the model to learn and simulate intricate function mapping relationships. In the absence of an activation function, irrespective of the number of layers in a neural network, the model is ultimately equivalent to a linear model, which would significantly restrict its expressive power. Gradient vanishing is a common issue where the gradient diminishes rapidly with the increase in layers during backpropagation, resulting in the weights in deeper layers of the network being updated very slowly. The derivative of the ReLU function is a constant 1 in positive intervals, which helps to alleviate the problem of vanishing gradients. The ReLU function is very simple to compute, with no complex mathematical operations, which makes it highly efficient in practical applications. The sparsity of the ReLU function also serves to mitigate the phenomenon of overfitting to a certain extent. Next, the image passes through a combination of \(1\times 1\), \(7\times 7\), and \(3\times 3\), in turn containing convolution kernels of different sizes. This is to capture features at different scales in the image. The \(1\times 1\) convolutional kernel is primarily responsible for capturing local details and texture information. Due to its small receptive field, the \(1\times 1\) convolutional kernel is capable of efficiently handling minor alterations in the image. The \(3\times 3\) convolutional kernel is primarily responsible for capturing medium-range features, such as edges and corners, thereby facilitating the extraction of distortions at the image edges with greater detail. The \(7\times 7\) convolutional kernel has the capacity to encompass a more expansive  
Figure 5: Lightweight attention mechanism. receptive field, thereby facilitating the capture of a broader range of contextual information. This is advantageous for the comprehension of global structures and patterns within an image. The combination of convolutional kernels of varying sizes enhances the adaptability of the model, enabling it to accommodate disparate input dimensions and feature scales. This adaptability is crucial for processing images with diverse resolutions and scales.