# Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models

arXiv: https://arxiv.org/abs/2508.00898 · HTML: https://mlsanity.com/paper/2508.00898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data](https://mlsanity.com/paper/1602.05875/) (2016)
- [Deep Frame Prediction for Video Coding](https://mlsanity.com/paper/1901.00062/) (2018)
- [PreCNet: Next-Frame Video Prediction Based on Predictive Coding](https://mlsanity.com/paper/2004.14878/) (2020)

## Abstract (arXiv)

In recent years, advances in Artificial Intelligence have significantly impacted computer science, particularly in the field of computer vision, enabling solutions to complex problems such as video frame prediction. Video frame prediction has critical applications in weather forecasting or autonomous systems and can provide technical improvements, such as video compression and streaming. Among Artificial Intelligence methods, Deep Learning has emerged as highly effective for solving vision-related tasks, although current frame prediction models still have room for enhancement. This paper evaluates several hybrid deep learning approaches that combine the feature extraction capabilities of autoencoders with temporal sequence modelling using Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (3D CNNs), and related architectures. The proposed solutions were rigorously evaluated on three datasets that differ in terms of synthetic versus real-world scenarios and grayscale versus color imagery. Results demonstrate that the approaches perform well, with SSIM metrics increasing from 0.69 to 0.82, indicating that hybrid models utilizing 3DCNNs and ConvLSTMs are the most effective, and greyscale videos with real data are the easiest to predict.