• Introduction
• Publication
• Getting Started
• Contributing
• License
• Contact

🌟 Pioneering the Future of Video Compression: The Wamboo Initiative at Harmony Valley 🌍

Harmony Valley embarked on its journey in April 2022, driven by a core mission to revolutionize video compression. Recognizing the substantial energy consumption associated with video data, the project proudly introduced two groundbreaking applications: the eco-conscious Wamboo app for Android users and the innovative Wamboo eco-cam. Notably, the publication "Green Video Compression for Metaverse: Lessons Learned from VP9 and HEVC," for SMPTE Media Technology Summit, rigorously examines motion estimation inefficiencies in these established standards while presenting viable enhancement approaches.

In tandem with our commitment to progress, we have released the comprehensive source codes for both Android applications and the motion estimation algorithms of VP9 and HEVC in Matlab. These resources include detailed energy efficiency metrics and computation count calculations. As part of our collaborative ethos, we offer these tools to all impassioned researchers, fostering their active participation in our collective objective.

Our primary goal is to enhance the VP9 and HEVC video compression algorithms to include our own energy-efficient video compression algorithm. This algorithm is intended for integration into the Wamboo Eco-compressor and Wamboo Eco-cam Android applications, available at Wamboo Eco-compressor and Wamboo Eco-cam, respectively.

For more information about the Harmony Valley project and its goals, visit the Harmony Valley project page.

You can find more details about the research for which this Matlab code was created and the findings in the publication:

• Title: "Green Video Compression for Metaverse: Lessons Learned from VP9 and HEVC"
• Conference: 2023 SMPTE Media Technology Summit
• Author: Natalia Molinero Mingorance

To get started with the HEVC Motion Estimation code, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/VP9-Motion-Estimation-Matlab.git

2. Open the Matlab project and explore the motion estimation algorithms.

3. Refer to the documentation and comments in the code for usage instructions and insights.

We welcome contributions from the community to help improve our research and tools. If you would like to contribute, please follow these steps:

1. Fork the repository by clicking the "Fork" button at the top right of the project repository page.

2. Clone your forked repository to your local machine:

   git clone https://github.com/your-username/VP9-Motion-Estimation-Matlab.git

3. Create a new branch for your contribution:

   git checkout -b your-branch-name

4. Make your desired changes to the codebase.

5. Commit your changes with a descriptive commit message:

   git commit -m "Add new feature"

6. Push your changes to your forked repository on GitHub:

   git push origin your-branch-name

7. Open a Pull Request (PR) by going to the original project repository and clicking the "New Pull Request" button. Describe your changes and submit the PR.

8. Your PR will be reviewed by project maintainers, and you can collaborate on any necessary updates.

This project is licensed under the Apache License 2.0.

For any questions or inquiries, please feel free to contact us at [email protected].