Video content has become a dominant form of media, with the demand for higher resolutions increasing daily. Whether for streaming platforms, video editing, or personal use, the ability to upscale video efficiently and effectively is crucial. In this blog post, we’ll explore how to develop a video upscaling AI using Amazon SageMaker, leveraging advanced models like ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks). We’ll cover the solution architecture, implementation in Amazon SageMaker, the initial experiments with TensorFlow, and our journey to implementing ESRGAN.

Solution Architecture

Before diving into the technical details, it’s essential to outline the overall architecture of our video upscaling solution. The solution involves several key components:

  1. Data Storage and Management: All video data is stored in Amazon S3. The raw videos (low resolution) and the upscaled videos (high resolution) are stored in different S3 buckets for easy access and management.
  2. Model Training and Inference: Amazon SageMaker is the core service for training our AI models. It provides a scalable environment for training, fine-tuning, and deploying our upscaling models.
  3. Model Deployment: Once the model is trained, it’s deployed using SageMaker Endpoints, allowing us to upscale videos on demand.
  4. Monitoring and Optimization: We use Amazon CloudWatch to monitor the training and inference processes, ensuring optimal performance and identifying areas for improvement.

Amazon SageMaker Implementation

Amazon SageMaker is a fully managed service that provides every necessary tool to build, train, and deploy machine learning models. Here’s how we implemented our video upscaling AI:

  1. Setting Up the Environment: We created a SageMaker notebook instance for data preprocessing and initial experiments. The notebook environment was configured with TensorFlow and PyTorch, as well as necessary libraries like OpenCV for handling video data.
  2. Data Preprocessing: Video frames were extracted from low-resolution videos using OpenCV and stored in Amazon S3. We divided the data into training and validation sets, ensuring a balanced dataset for model training.
  3. Training the Model: We initially used a primary convolutional neural network (CNN) for video upscaling as a baseline. The model was trained on SageMaker using a custom TensorFlow script, utilizing GPU instances for faster training. The training process involved multiple iterations, adjusting hyperparameters to optimize performance.
  4. Evaluation and Metrics: After training, the model was evaluated on the validation set using metrics like PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). The results guided further enhancements and model selection.

Initial Upscaling Experiment with TensorFlow

Our initial approach involved using TensorFlow to build and train a basic upscaling model. The model consisted of several convolutional layers designed to increase the resolution of the input video frames. While this approach provided a decent upscaling effect, the results were far from what is expected in high-end applications.

The model needed help with preserving finer details and often introduced artifacts. However, this experiment was crucial as it provided a solid foundation to build upon and a benchmark against which more advanced models could be compared.

Advancing to ESRGAN

Recognizing the limitations of our initial model, we moved on to ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks), a state-of-the-art model known for producing highly realistic images during upscaling. Implementing ESRGAN involved several steps:

  1. Model Adaptation: The ESRGAN architecture was adapted to handle video data by modifying its input pipeline to process consecutive video frames. This adaptation helps maintain temporal consistency across frames, which is crucial in video processing.
  2. Training on SageMaker: Training ESRGAN is computationally intensive. We utilized Amazon SageMaker’s distributed training capabilities, using multiple GPU instances to speed up the process. The model was trained on a high-resolution video dataset and fine-tuned for video upscaling.
  3. Inference and Testing: After training, the ESRGAN model was deployed using SageMaker Endpoints. The testing involved upscaling various low-resolution videos and comparing the results with the original high-resolution versions. The ESRGAN model significantly outperformed the initial TensorFlow-based model, producing sharper and more detailed upscaled videos.

Conclusion & Thoughts

Developing a video upscaling AI with Amazon SageMaker is a powerful approach that leverages cutting-edge technologies and scalable cloud infrastructure. Our journey from a basic CNN model to ESRGAN highlights the importance of iterative development and the need for advanced models to achieve high-quality video upscaling.

Amazon SageMaker proved invaluable, providing the necessary infrastructure and flexibility to experiment with different models and scale the training process. As video resolution demands continue to grow, having a robust upscaling solution like ESRGAN on SageMaker positions you to meet these demands effectively.

References

Create super-resolution for legacy media content at scale with generative AI and AWS

Guidance for Hyperscale Media Super Resolution on AWS