Physics Maths Engineering
Peer Reviewed
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++.
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.
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.
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.
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.
The technique offers:
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.
This technique is ideal for:
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.
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.
Future research could focus on:
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Total | 583 | 583 |
Show by month | Manuscript | Video Summary |
---|---|---|
2025 April | 4 | 4 |
2025 March | 79 | 79 |
2025 February | 45 | 45 |
2025 January | 56 | 56 |
2024 December | 57 | 57 |
2024 November | 57 | 57 |
2024 October | 56 | 56 |
2024 September | 41 | 41 |
2024 August | 42 | 42 |
2024 July | 30 | 30 |
2024 June | 25 | 25 |
2024 May | 32 | 32 |
2024 April | 46 | 46 |
2024 March | 13 | 13 |
Total | 583 | 583 |