A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
Source abstract
Abstract
Lossless image compression plays a vital role in improving data storage and transmission efficiency without compromising data integrity. However, the throughput of current lossless compression and decompression systems remains limited and is unable to meet the growing demands of high-speed data transfer. To address this challenge, a previously proposed hybrid lossless compression and decompression algorithm has been implemented on an FPGA platform. This implementation significantly improves processing speed and efficiency. A multi-core system architecture is introduced, utilizing the processing system (PS) and programmable logic (PL) of a Xilinx Zynq-706 evaluation board. The PS handles coordination. The PL performs compression and decompression using multiple cores. Each core can process up to eight image tiles at the same time. The compression process is designed with a four-stage pipeline, and decompression is managed by a dynamic state machine to ensure optimized control. The parallel architecture and innovative algorithm design enable high-throughput operation, achieving compression and decompression rates of 480 Msubpixels/s and 372 Msubpixels/s, respectively. Through this work, a practical and high-performance solution for real-time lossless image compression is demonstrated.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Data Compression Techniques · Image and Signal Denoising Methods · Digital Filter Design and Implementation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China
Funding
Key Program of the Chinese Academy of Sciences
References (32)
Show source-provided references
1. Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., and Gool, L.V. (2019, January 15–20). Practical full resolution learned lossless image compression. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.01088 ↗
2. Si, Z., and Shen, K. (2016). Research on the WebP image format. Advanced Graphic Communications, Packaging Technology and Materials, Springer.10.1007/978-981-10-0072-0_35 ↗
3. A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding10.1109/TITB.2009.2022971 ↗
4. Zhang, D., Yang, M., Tao, J., Wang, Y., Liu, B., and Bukhari, D. (2016, January 20–25). Extraction of tongue contour in real-time magnetic resonance imaging sequences. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.10.1109/ICASSP.2016.7471813 ↗
5. Lossless Compression Method for the Magnetic and Helioseismic Imager (MHI) Payload10.1088/1674-4527/ad2cd4 ↗
6. Evaluation of JPEG-LS, the new lossless and controlled-lossy still image compression standard, for compression of high-resolution elevation data10.1109/36.957293 ↗
7. Verstockt, S., De Bruyne, S., Poppe, C., Lambert, P., and Van de Walle, R. (2009, January 2–4). Multi-view Object Localization in H.264/AVC Compressed Domain. Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, Genoa, Italy.10.1109/AVSS.2009.24 ↗
8. Albalawi, U., Mohanty, S.P., and Kougianos, E. (2015, January 8–10). A Hardware Architecture for Better Portable Graphics (BPG) Compression Encoder. Proceedings of the 2015 IEEE International Symposium on Nanoelectronic and Information Systems, Didcot, UK.10.1109/iNIS.2015.12 ↗
9. Wu, X., Choi, W.K., and Bao, P. (1997, January 25–27). L/sub /spl infin//-constrained high-fidelity image compression via adaptive context modeling. Proceedings of the DCC ’97. Data Compression Conference, Snowbird, UT, USA.
10. Öztürk, E., and Mesut, A. (2021, January 15–17). Performance Evaluation of JPEG Standards, WebP and PNG in Terms of Compression Ratio and Time for Lossless Encoding. Proceedings of the 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara, Turkey.10.1109/UBMK52708.2021.9558922 ↗
11. The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS10.1109/83.855427 ↗
12. Adaptive Pipeline Hardware Architecture Design and Implementation for Image Lossless Compression/Decompression Based on JPEG-LS10.1109/ACCESS.2024.3350344 ↗
13. Dong, X., and Li, P. (2022, January 18–20). Implementation of A Real-Time Lossless JPEG-LS Compression Algorithm Based on FPGA. Proceedings of the 2022 14th International Conference on Signal Processing Systems (ICSPS), Zhenjiang, China.10.1109/ICSPS58776.2022.00096 ↗
14. Wang, X., Gong, L., Wang, C., Li, X., and Zhou, X. (2021, January 24–27). UH-JLS: A Parallel Ultra-High Throughput JPEG-LS Encoding Architecture for Lossless Image Compression. Proceedings of the 2021 IEEE 39th International Conference on Computer Design (ICCD), Virtual.10.1109/ICCD53106.2021.00060 ↗
15. Ferretti, M., and Boffadossi, M. (2004, January 23–26). A parallel pipelined implementation of LOCO-I for JPEG-LS. Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, UK.10.1109/ICPR.2004.1334311 ↗
16. Hardware implementation of a lossless image compression algorithm using a field programmable gate array
17. A Fully Pipelined Architecture for the LOCO-I Compression Algorithm10.1109/TVLSI.2008.2009188 ↗
18. Mert, Y.M. (2015, January 23–24). FPGA-based JPEG-LS encoder for onboard real-time lossless image compression. Proceedings of the Satellite Data Compression, Communications, and Processing XI, Baltimore, MD, USA.10.1117/12.2177882 ↗
19. Varshney, A., Suneetha, K., and Yadav, D.K. (2024, January 29–30). Analyzing the Performance of Different Compactor Techniques in Data Compression Source Coding. Proceedings of the 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC), Debre Tabor, Ethiopia.10.1109/ICOCWC60930.2024.10470923 ↗
20. Akhtar, M.B., Qureshi, A.M., and ul Islam, Q. (September, January 31). Optimized run length coding for jpeg image compression used in space research program of IST. Proceedings of the International Conference on Computer Networks and Information Technology, Paphos, Cyprus.
21. Run-length encodings (Corresp.)10.1109/TIT.1966.1053907 ↗
22. (2024, December 20). xilinx. Vivado Design Suite User Guide Using Constraints (UG903). Available online: https://docs.amd.com/r/en-US/ug903-vivado-using-constraints.
23. (2019, August 06). xilinx. ZC706 Evaluation Board for the Zynq-7000 XC7Z045 SoC User Guide (UG954). Available online: https://docs.amd.com/v/u/en-US/ug954-zc706-eval-board-xc7z045-ap-soc.
24. (2018, July 02). xilinx. Zynq-7000 SoC Data Sheet: Overview (DS190). Available online: https://docs.amd.com/v/u/en-US/ds190-Zynq-7000-Overview.
25. Chrabaszcz, P., Loshchilov, I., and Hutter, F. (2017). A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets. arXiv.
26. Chen, S., Han, Z., Dai, E., Jia, X., Liu, Z., Liu, X., Zou, X., Xu, C., Liu, J., and Tian, Q. (2020, January 14–19). Unsupervised Image Super-Resolution with an Indirect Supervised Path. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00242 ↗
27. Ma, G., Chai, Y., Jiang, T., Lu, M., and Chen, T. (2024). TinyLIC-High efficiency lossy image compression method. arXiv.
28. Minnen, D., Toderici, G., Covell, M., Chinen, T., Johnston, N., Shor, J., Hwang, S.J., Vincent, D., and Singh, S. (2017, January 17–20). Spatially adaptive image compression using a tiled deep network. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.10.1109/ICIP.2017.8296792 ↗
29. The JPEG 2000 still image compression standard10.1109/79.952804 ↗
30. High-Throughput Architecture for Both Lossless and Near-lossless Compression Modes of LOCO-I Algorithm10.1109/TCSVT.2018.2881040 ↗
31. Savakis, A., and Piorun, M. (2002, January 22–25). Benchmarking and hardware implementation of JPEG-LS. Proceedings of the International Conference on Image Processing, Rochester, NY, USA.
32. Deng, L., and Huang, Z. (2015, January 27–29). The FPGA design of JPEG-LS image lossless decompression IP core. Proceedings of the 2015 Chinese Automation Congress (CAC), Wuhan, China.