## 2 Related Work
It enhances anomaly recognition, proves to be more resistant to label noise, and provides an additional anomaly score to refine predictions from classifiers that are sensitive to noisy labels. In [43], a Temporal Context Aggregation (TCA) module for efficient context modeling and a Prompt-Enhanced Learning (PEL) module for enhanced semantic discriminability are demonstrated. The TCA module captures complete contextual information, while the PEL module incorporates semantic priors using knowledge-based prompts to improve discriminative capacity and maintain separability between anomaly sub-classes. Additionally, a Score Smoothing (SS) module is introduced in the testing phase to reduce false alarms. In [44], a U-Net-like structure is implemented to effectively capture both local and global temporal dependencies in a unified manner. The encoder hierarchically learns global dependencies on top of local ones, and the decoder propagates this global information back to the segment level for classification. Recent research has focused on addressing anomalies in extremely low-resolution videos [25; 45; 46; 47]. Techniques such as Inverse Super-Resolution (ISR), initially introduced by [PERSON] et al. [45], aim to identify optimal image modifications for extracting additional information from low-resolution images. Additionally, multi-Siamese loss functions have been proposed to maximize data utilization from a collection of low-resolution images. [PERSON] et al. [46] developed a semi-coupled two-stream network that leverages high-resolution images to assist with training a low-resolution network. [PERSON] et al. [47] demonstrated that using high-resolution images improves low-resolution recognition by incorporating a two-stream neural network architecture that takes high-resolution images as inputs. Their approach, sharing convolutional filters between low- and high-resolution networks, significantly enhanced performance. In addition, [PERSON] et al. [48] proposed the TinyVIRAT dataset for natural low-resolution videos and presented a gradual generative technique for enhancing the quality of low-resolution events. Super-resolution techniques have also found success in various applications such as low-resolution face verification, small object detection, person re-identification, and activity recognition [49; 50; 51; 52]. For instance, [PERSON] et al. [50] introduced an identity-preserving super-resolution approach for face verification at very low resolutions, and [PERSON] et al. [51] developed a multitask generative adversarial network for small object detection. In summary, the field of video anomaly detection has witnessed diverse advancements, from Bayesian deep learning to convolutional models, recurrent neural networks, and spatial-temporal graph attention networks. Our study addresses the challenge of detecting anomalies in multi-class scenarios within low-quality surveillance videos and showcases improved classification performance compared to interpolation-based strategies. The integration of novel super-resolution techniques and a two-stream architecture forms the backbone of our methodology and contributes to the evolution of video anomaly recognition in complex real-world scenarios. While the literature review reflects significant progress in video anomaly detection, there is a significant research gap that our study seeks to fill. Existing approaches have primarily focused on either high-quality video scenarios or have addressed anomalies in a binary manner, both of which are insufficient for real-world applications. The combination of novel super-resolution techniques and a two-stream architecture, as proposed in our methodology, represents a novel approach to closing this gap. Our research contributes to the evolving landscape of video anomaly recognition by providing a tailored solution to the complexities of multi-class scenarios and low-quality surveillance videos within 5G and IoT environments.