Physics Maths Engineering
FitDepth: fast and lite 16-bit depth image compression algorithm
Peer Reviewed
Abstract
Abstract This article presents a fast parallel lossless technique and a lossy image compression technique for 16-bit single-channel images. Nowadays, such techniques are “a must” in robotics and other areas where several depth cameras are used. Since many of these algorithms need to be run in low-profile hardware, as embedded systems, they should be very fast and customizable. The proposal is based on the consideration of depth images as surfaces, so the idea is to split the image into a set of polynomial functions that each describes a part of the surface. The developed algorithm herein proposed can achieve a similar—or better—compression rate and especially higher speed rates than the existing techniques. It also has the potential of being fully parallelizable and to run on several cores. This feature, compared to other approaches, makes it useful for handling and streaming multiple cameras simultaneously. The algorithm is assessed in different situations and hardware. Its implementation is rather simple and is carried out with LIDAR captured images. Therefore, this work is accompanied by an open implementation in C++.
Key Questions
What is the focus of this study?
This study introduces a fast and efficient image compression technique for 16-bit single-channel images, commonly used in robotics and depth cameras. The method is designed to be lightweight, customizable, and suitable for low-profile hardware like embedded systems.
Why is image compression important in robotics?
In robotics, multiple depth cameras often generate large amounts of data. Efficient compression is essential to handle and stream this data in real-time, especially on low-profile hardware with limited processing power.
What is unique about the proposed compression technique?
The technique treats depth images as surfaces and splits them into polynomial functions, each describing a part of the surface. This approach achieves high compression rates, faster processing speeds, and is fully parallelizable, making it ideal for multi-camera setups.
How does the algorithm achieve high speed?
The algorithm is designed to run on multiple cores, enabling parallel processing. This makes it significantly faster than traditional methods, especially when handling multiple cameras simultaneously.
What are the key benefits of this technique?
The technique offers:
- High compression rates, similar to or better than existing methods.
- Faster processing speeds, ideal for real-time applications.
- Full parallelizability, allowing it to run on multiple cores.
- Simple implementation, with an open-source C++ codebase.
How was the algorithm tested?
The algorithm was tested on LIDAR-captured images and evaluated in various scenarios and hardware setups. It demonstrated superior compression rates and speed compared to existing techniques.
What are the practical applications of this technique?
This technique is ideal for:
- Robotics, where multiple depth cameras are used.
- Real-time streaming and data handling in embedded systems.
- Applications requiring fast, efficient compression of 16-bit images.
How does this compare to traditional compression methods?
Traditional methods often struggle with speed and efficiency on low-profile hardware. This technique outperforms them by offering faster processing, higher compression rates, and the ability to run in parallel on multiple cores.
Is the implementation available to the public?
Yes, the implementation is open-source and available in C++. This makes it accessible for researchers and developers to use, modify, and integrate into their projects.
What are the future directions for this research?
Future research could focus on:
- Extending the technique to other types of images, such as multi-channel or 3D data.
- Optimizing the algorithm for specific hardware platforms.
- Integrating it with AI for automated compression and analysis.
