To extend the proposed PCQA method to handle longer video sequences and more diverse content beyond the current datasets, several strategies can be implemented. One approach is to incorporate temporal information processing mechanisms into the model architecture. This can involve utilizing recurrent neural networks (RNNs) or transformers with attention mechanisms to capture temporal dependencies across frames in longer video sequences. By incorporating these components, the model can effectively analyze and assess the quality of videos with varying lengths.
Furthermore, data augmentation techniques can be employed to simulate longer video sequences during training. This can involve techniques such as frame interpolation, where additional frames are generated between existing frames to create longer sequences. By training the model on augmented data with varying lengths, it can learn to generalize better to videos of different durations.
Additionally, the model can benefit from pre-training on a diverse range of video datasets with varying lengths and content types. By exposing the model to a wide array of video data during pre-training, it can learn robust representations that generalize well to different video content. Fine-tuning the model on specific datasets with longer sequences can further enhance its performance on such data.