# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 1 Introduction  
The proliferation of online ultra-high-definition video has significantly increased data volumes, challenging existing bandwidth resources. It has become a crucial research priority to enhance video coding strategies for efficiency and to maintain visual quality. Traditional video encoders rely on heuristic rules and human visual system characteristics to minimize temporal and spatial redundancies. With advancements in deep learning, neural networks are increasingly applied in image and video processing. However, advancements in deep learning have led to the increased application of deep neural networks (DNNs) in image and video processing. Initially, efforts centered on replacing specific modules of traditional video encoders with neural networks, including prediction [1], residual [2], mode decision [3], and discrete cosine transform (DCT) [4] modules. Despite these integrations, this approach often struggles to achieve an optimal balance between coding efficiency and visual fidelity, which has prompted the development of fully neural network-based models. [PERSON] et al. [5] introduced Deep Video Compression (DVC), a fully deep learning-based framework utilizing optical flow estimation and autoencoder-based networks for motionand residual information compression. However, DVC's single reference frame prediction limits its utilization of temporal correlations. To address this, [PERSON] et al. [6] proposed the Multiple Frames Prediction for Learned Video Compression (M-LVC) model, which enhances temporal correlations through multi-reference frame motion compensation. Further improvements were achieved by [PERSON] et al. [7], who converted the predictive coding framework to a conditional coding framework. This approach takes rich spatio-temporal contextual information as conditional input to optimize the coding process of video frames. Then, the DCVC model proposed by [PERSON] et al. [7] employs context encoding to overcome the limitations of residual coding. Subsequently, DCVC-HEM [8] introduced a powerful parallel-friendly entropy model for improved probability distribution prediction. Further advancements by [PERSON] et al. with the DCVC-DC model [9] demonstrated superior performance over traditional codecs in both RGB and YUV420 color spaces. Despite the successes of DCVC models in contextual coding, they typically require large changes to the coding framework, which may limit their applicability to existing video coding standards. To address this, [PERSON] et al. [10] proposed Feature Space Deep Video Compression (FVC) to project video frames from the pixel space to feature space. Compared to conditional coding frameworks, feature space mapping enables a more efficient representation, which significantly reduces pixel-domain redundancy and improves compression efficiency while maintaining sensitivity and adaptability to visual content. Although these methods have advanced video-coding algorithms, there remains a lack of framework for optimizing encoding from an end-to-end perspective. For instance, [PERSON] et al. [5] proposed an end-to-end video encoding method which employs two neural networks to compress both motion and residual information. Similarly, [PERSON] et al. [11] first introduced a semantic communication-based hybrid video codec, which leverages the intra coding capabilities of Versatile Video Coding (VVC) to encode key frames. These key frames provide context for semantic communication, while residuals are encoded to enhance the fidelity of the output frames. These methods excel in reducing redundancy and enhancing compression efficiency, but they may lack sufficient flexibility to adapt to varying video content and encoding requirements. Video preprocessing techniques, which involve a series of preliminary operations on video data before coding, play a crucial role in the optimization process. Recent advancements in learning-based video encoding preprocessing techniques have shown promising results. [PERSON] et al.