Real-Time Adaptive Rate Control for Hyperspectral Image Compression on FPGA
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 7 |
| Year of Publication : 2025 |
| Authors : D. Balaji, S. Shiyamala |
10.14445/23488379/IJEEE-V12I7P117
How to Cite?
D. Balaji, S. Shiyamala, "Real-Time Adaptive Rate Control for Hyperspectral Image Compression on FPGA," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 7, pp. 238-252, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I7P117
Abstract:
The spectral analysis capabilities of Hyperspectral imaging produce large datasets that create problems when storing and transmitting the data, along with processing it in real-time. Traditional compression approaches that use transform-based and deep learning methods either need versatile adaptation features or considerable computational power. A real-time adaptive rate control hyperspectral image compression system based on FPGA and software co-design features the proposal. The proposed method optimizes compression efficiency through three elements, which include adaptive quantization and entropy coding and a dynamic rate control system. The method processes hyperspectral data at speeds faster than 25 frames per second while using less than 5 watts of power to deliver compression ratios between 10:1 and 50:1 and PSNR values from 35-45 dB, along with SSIM measures between 0.92 and 0.98. The approach achieves a lower bit rate level of 30–50% when compared to earlier research methods while delivering superior visual results. The proposed solution delivers an effective power-saving method for real-time hyperspectral image compression, which benefits satellite and UAV applications.
Keywords:
Field-Programmable Gate Array, Structural similarity index, Unmanned aerial vehicle, Airborne visible/infrared imaging spectrometer, Hyperspectral digital imagery collection, Rate-distortion optimization Experiment, Band sequential.
References:
[1] Daniel Vorhaug et al., “Development and Integration of CCSDS 123.0-b-2 FPGA Accelerator for Hypso-1,” Proceedings of the 9th International Workshop on On-Board Payload Data Compression (OBPDC), 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Chunyan Yu et al., “Distillation-Constrained Prototype Representation Network for Hyperspectral Image Incremental Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xichuan Zhou et al., “BTC-Net: Efficient Bit-Level Tensor Data Compression Network for Hyperspectral Image,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Niklas Sprengel, Martin Hermann Paul Fuchs, and Begüm Demir, “Learning-Based Hyperspectral Image Compression Using a Spatio-Spectral Approach,” EGU General Assembly, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Kazi Mohammad Abidur Rahman et al., “ISFD: Efficient and Fault-Tolerant In-System-Failure-Detection for LP FPGA-based Smart-Sensors in Space Expeditions,” 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Abu Dhabi, United Arab Emirates, pp. 74-83, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Maryam Hatami et al., “Noninvasive Tracking of Embryonic Cardiac Dynamics and Development with Volumetric Optoacoustic Spectroscopy,” Advanced Science, vol. 11, no. 22, pp. pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Duc Khai Lam, “Real-Time Lossless Image Compression by Dynamic Huffman Coding Hardware Implementation,” Journal of Real-Time Image Processing, vol. 21, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jhilam Jana et al., “FPGA Implementation of Compact and Low-Power Multiplierless Architectures for DWT and IDWT,” Journal of Real-Time Image Processing, vol. 21, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Dongmei Xue et al., “DBVC: An End-to-End 3-D Deep Biomedical Video Coding Framework,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 4, pp. 2922-2933, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jiahui Qu et al., “A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2235-2249, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Danfeng Hong et al., “Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Hanzheng Wang et al., “Transformer-based Band Re-Grouping with Feature Refinement for Hyperspectral Object Tracking,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wenrui Cai, Qingjie Liu, and Yunhong Wang, “HIP-track: Visual Tracking with Historical Prompts,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 19258-19267, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zedu Chen et al., “SiamBAN: Target-Aware Tracking with Siamese Box Adaptive Network,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 5158-5173, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Marc Masana et al., “Class-Incremental Learning: Survey and Performance Evaluation on Image Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 5, pp. 5513-5533, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Qiang Zhang et al., “Combined Deep Priors with Low-Rank Tensor Factorization for Hyperspectral Image Restoration,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Shaoxiong Xie et al., “VP-HOT: Visual Prompt for Hyperspectral Object Tracking,” 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Athens, Greece, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Grzegorz Ulacha, and Mirosław Łazoryszczak, “Lossless Image Coding using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients,” Entropy, vol. 25, no. 1, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xiyu Sun et al., “A Lossless Image Compression and Encryption Algorithm Combining JPEG-LS Neural Network and Hyperchaotic System,” Nonlinear Dynamics, vol. 111, no. 16, pp. 15445-15475, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sang-Ho Hwang et al., “Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing,” Sensors, vol. 23, no. 20, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Vijay Joshi, and J. Sheeba Rani, “A Simple Lossless Algorithm for On-Board Satellite Hyperspectral Data Compression,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Lili Zhang, “FPGA Design and Implementation of Real-Time Lossless Compression System for Spaceborne Imagery,” Journal of Experimental Technology and Management, vol. 40, no. 2, pp. 57-62, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Ji Linfeng, Hua Guoxiang, and Xiao Yang, “Research on Progressive Transmission Image Restoration Algorithm for Beidou Short Message,” Electronic Measurement Technology, vol. 46, no. 20, pp. 133-139, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Tianpeng Pan et al., “A Coupled Compression Generation Network for Remote-Sensing Images at Extremely Low Bitrates,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Satvik Agrawal et al., “Hyperspectral Image Compression Using Modified Convolutional Autoencoder,” International Journal of Computer Information Systems and Industrial Management Applications, vol. 15, pp. 396-407, 2023.
[Google Scholar] [Publisher Link]
[26] Jannick Kuester et al., “Adaptive Two-Stage Multisensor Convolutional Autoencoder Model for Lossy Compression of Hyperspectral Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Swalpa Kumar Roy et al., “Multimodal Fusion Transformer for Remote Sensing Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] M. Vögtli et al., “Hyperthun'22: A Multi-Sensor Multi-Temporal Camouflage Detection Campaign,” IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 2153-2156, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Dongmei Xue et al., “AiWave: Volumetric Image Compression with 3-D Trained Affine Wavelet-like Transform,” IEEE Transactions on Medical Imaging, vol. 42, no. 3, pp. 606-618, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Anasua Banerjee, and Debajyoty Banik, “Pooled Hybrid-Spectral for Hyperspectral Image Classification,” Multimedia Tools and Applications, vol. 82, no. 7, pp. 10887-10899, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Vamshi Krishna Munipalle, Usha Rani Nelakuditi, and Rama Rao Nidamanuri, “Agricultural Crop Hyperspectral Image Classification Using Transfer Learning,” 2023 International Conference on Machine Intelligence for Geo Analytics and Remote Sensing (MIGARS), Hyderabad, India, vol. 1, pp. 1-4, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Jae-Jin Park et al., “Aerial Hyperspectral Remote Sensing Detection for Maritime Search and Surveillance of Floating Small Objects,” Advances in Space Research, vol. 72, no. 6, pp. 2118-2136, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Harshita Mangotra et al., “Hyperspectral Imaging for Early Diagnosis of Diseases: A Review,” Expert Systems, vol. 40, no. 8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Lingxi Liu et al., “Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review,” Sensors, vol. 23, no. 5, pp. 1-25, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Aswathi Soni et al., “Hyperspectral Imaging and Machine Learning in Food Microbiology: Developments and Challenges in Detection of Bacterial Fungal and Viral Contaminants,” Comprehensive Reviews in Food Science and Food Safety, vol. 21, no. 4, pp. 3717-3745, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] A. Nisha, and A. Anitha, “Current Advances in Hyperspectral Remote Sensing in Urban Planning,” 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India, pp. 94-98, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Shrish Bajpai et al., “A Low Complexity Hyperspectral Image Compression Through 3D Set Partitioned Embedded Zero Block Coding,” Multimedia Tools and Applications, vol. 81, no. 1, pp. 841-872, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Adduru U.G. Sankararao, and P. Rajalakshmi, “UAV Based Hyperspectral Remote Sensing and CNN for Vegetation Classification,” IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia pp. 7737-7740, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Behnood Rasti et al., “Image Restoration for Remote Sensing: Overview and Toolbox,” IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 2, pp. 201-230, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Qiang Zhang et al., “Cooperated Spectral Low-Rankness Prior and Deep Spatial Prior for HSI Unsupervised Denoising,” IEEE Transactions on Image Processing, vol. 31, pp. 6356-6368, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Asad Mahmood, and Michael Sears, “Per-Pixel Noise Estimation in Hyperspectral Images,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]