The remote sensing imagery generated by the onboard sensors of various satellites are contained in several wavelength bands. These bands constitute high resolution that occupy a lot of storage space and memory. Moreover, the downlink transmission to the ground station for these images requires high bandwidth. Therefore, the high-resolution satellite imagery needs to be compressed so that more number of images can be transmitted without any significant loss of information while maintaining the quality of the reconstructed image. This paper aims to compress satellite images using a hybrid methodology of Huffman and run-length coding in JPEG 2000. In addition, JPEG 2000 uses wavelet transform, which gives higher compression ratios and better resolution than JPEG. This methodology written in VHDL (very high-speed integrated circuit hardware description language) is implemented in hardware with PCI add-on card that includes buffers, control logic, application-specific integrated circuit (ASIC), Field Programmable Gate Array (FPGA), and memory devices. The results of this wavelet transform–based image compression technology show a compression ratio of approximately 3.4, with minimum degradation in the data quality of the reconstructed image. The entire operation is done at clock frequencies of 2 MHz and 50 MHz. The operation of compression for eight lines of an image consisting of 6000 pixels per line has been completed in less than 3 ms. The design utilizes only 592 logic elements out of the available 12,000 logic elements on the target FPGA and thus consumes less than 1% of the memory space.

Keywords: Image compression, wavelet transform, Huffman coding, run-length coding

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1. Świercz E. Image encryption algorithms based on wavelet decomposition and encryption of compressed data in wavelet domain. Przegląd Elektrotechniczny. 2018; 94 (2): 81–85.
2. Singh G, Goel AK. Face detection and recognition system using digital image processing. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, March 5–7, 2020. pp. 348–352.
3. Wei J, Han J, Cao S. Satellite IoT edge intelligent computing: a research on architecture. Electronics. 2019; 8 (11): 1247.
4. Gómez-Valencia EM, Trejos S, Velez-Zea A, Barrera-Ramírez JF, Torroba R. Quality guided alternative holographic data representation for high performance lossy compression. J Optics. 2021; 23 (7): 075702.
5. Ruiz-Rosero J, Ramirez-Gonzalez G, Khanna R. Field programmable gate array applications—a scientometric review. Computation. 2019; 7 (4): 63.
6. Megala G. State-of-the-art in video processing: compression, optimization and retrieval. Turk J Computer Math Educ. 2021; 12 (5):: 1256–1272.
7. Evans RD, Liu L, Aamodt TM. JPEG-ACT: accelerating deep learning via transform-based lossy compression. In: 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), Valencia, Spain, May 30–June 3, 2020. pp. 860–873.
8. Hussain AJ, Al-Fayadh A, Radi N. Image compression techniques: a survey in lossless and lossy algorithms. Neurocomputing. 2018; 300: 44–69.
9. Rabie T, Baziyad M, Kamel I. Enhanced high capacity image steganography using discrete wavelet transform and the Laplacian pyramid. Multimedia Tools Appl. 2018; 77 (18): 23673–23698.
10. Tan L, Zeng Y, Zhang W. Research on image compression coding technology. J Phys Conf Ser. 2019; 1284 (1): 012069.
11. Hurley C, Mclean J. Wavelet: Analysis and Methods. Waltham, UK: ED-Tech Press; 2018.
12. Yadav P. A brief description of wavelet and wavelet transforms and their applications. Int J Stat Appl Math. 2018; 3 (1): 266–271.
13. Polat C, Özerdem MS. Introduction to wavelets and their applications in signal denoising. Bitlis Eren Univ J Sci Technol. 2018; 8 (1): 1–10.
14. Amin HU, Yusoff MZ, Ahmad RF. A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomed Signal Process Control. 2020; 56: 101707.
15. Krishnamurthi R, Gopinathan D. Wavelet transformation and machine learning techniques for digital signal analysis in IoT systems. In: Tanwar S, Nayyar A, Rameshwar R. Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead. Boca Raton, FL, USA: CRC Press; 2021. pp. 339–359.
16. Wani NS, Singh RP, Nemade MU. Detection, classification and localization of faults of transmission lines using wavelet transform and neural network. Int J Appl Eng Res. 2018; 13 (1): 98–106.
17. Fashi AA, Samiei MHV, Teixeira A. Design of a visible light photonic chip for Haar transform based optical compression. Optik. 2020; 217: 164929.
18. Monika R, Hemalatha R, Radha S. Energy efficient surveillance system using WVSN with reweighted sampling in modified fast Haar wavelet transform domain. Multimedia Tools Appl. 2018; 77 (23): 30187–30203.
19. Narayana PS, Khan AM. MRI image compression using multiple wavelets at different levels of discrete wavelets transform. J Phys Conf Ser. 2020; 1427 (1): 012002.
20. Brahimi T, Khelifi F, Kacha A. An efficient JPEG-2000 based multimodal compression scheme. Multimedia Tools Appl. 2021; 80 (14): 21241–21260.
21. Khandwani FI, Ajmire PE. A survey of lossless image compression techniques. Int J Electr Electron Computer Sci Eng. 2018; 5 (1): 39–42.
22. Moffat A. Huffman coding. ACM Comput Surveys (CSUR). 2019; 52 (4): 1–35.
23. Liu X, An P, Chen Y, Huang X. An improved lossless image compression algorithm based on Huffman coding. Multimedia Tools Appl. 2022; 81 (4): 4781–4795.
24. Tian J, Rivera C, Di S, Chen J, Liang X, Tao D, Cappello F . Revisiting Huffman coding: toward extreme performance on modern GPU architectures. In: 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, OR, USA, May 17–21, 2021. pp. 881–891.
25. Razaq U, Lizhong X, Li C, Usman M. Evolution and advancement of arithmetic coding over four decades. Open J Sci Technol. 2020; 3 (3): 194–236.
26. Rahman M, Hamada M. Lossless image compression techniques: a state-of-the-art survey. Symmetry. 2919; 11 (10): 1274.
27. Rajan PVS, Fred AL. An efficient compound image compression using optimal discrete wavelet transform and run length encoding techniques. J Intell Syst. 2019; 28 (1): 87–101.
28. Gopinath A, Ravisankar M. Comparison of lossless data compression techniques. In: 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, February 26–28, 2020. pp. 628–633.
29. Gupta N, Vijay R, Gupta HK. Performance evaluation of symmetrical encryption algorithms with wavelet based compression technique. EAI Endorsed Trans Scalable Inform Syst. 2020; 7 (28): e8.
30. Ahmed MA, Aljumah A, Gulam Ahmad MG. Design and implementation of a direct memory access controller for embedded applications. Int J Technol. 2019; 10 (2): 309–319.
31. Yin T, Xu C, Lin L, Jing K . A SiC MOSFET and Si IGBT hybrid modular multilevel converter with specialized modulation scheme. IEEE Trans Power Electron. 2020; 35 (12): 12623–12628.

The remote sensing imagery generated by the onboard sensors of various satellites are contained in several wavelength bands. These bands constitute high resolution that occupy a lot of storage space and memory. Moreover, the downlink transmission to the ground station for these images requires high bandwidth. Therefore, the high-resolution satellite imagery needs to be compressed so that more number of images can be transmitted without any significant loss of information while maintaining the quality of the reconstructed image. This paper aims to compress satellite images using a hybrid methodology of Huffman and run-length coding in JPEG 2000. In addition, JPEG 2000 uses wavelet transform, which gives higher compression ratios and better resolution than JPEG. This methodology written in VHDL (very high-speed integrated circuit hardware description language) is implemented in hardware with PCI add-on card that includes buffers, control logic, application-specific integrated circuit (ASIC), Field Programmable Gate Array (FPGA), and memory devices. The results of this wavelet transform–based image compression technology show a compression ratio of approximately 3.4, with minimum degradation in the data quality of the reconstructed image. The entire operation is done at clock frequencies of 2 MHz and 50 MHz. The operation of compression for eight lines of an image consisting of 6000 pixels per line has been completed in less than 3 ms. The design utilizes only 592 logic elements out of the available 12,000 logic elements on the target FPGA and thus consumes less than 1% of the memory space.

Keywords: Image compression, wavelet transform, Huffman coding, run-length coding

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Świercz E. Image encryption algorithms based on wavelet decomposition and encryption of compressed data in wavelet domain. Przegląd Elektrotechniczny. 2018; 94 (2): 81–85.
2. Singh G, Goel AK. Face detection and recognition system using digital image processing. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, March 5–7, 2020. pp. 348–352.
3. Wei J, Han J, Cao S. Satellite IoT edge intelligent computing: a research on architecture. Electronics. 2019; 8 (11): 1247.
4. Gómez-Valencia EM, Trejos S, Velez-Zea A, Barrera-Ramírez JF, Torroba R. Quality guided alternative holographic data representation for high performance lossy compression. J Optics. 2021; 23 (7): 075702.
5. Ruiz-Rosero J, Ramirez-Gonzalez G, Khanna R. Field programmable gate array applications—a scientometric review. Computation. 2019; 7 (4): 63.
6. Megala G. State-of-the-art in video processing: compression, optimization and retrieval. Turk J Computer Math Educ. 2021; 12 (5):: 1256–1272.
7. Evans RD, Liu L, Aamodt TM. JPEG-ACT: accelerating deep learning via transform-based lossy compression. In: 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), Valencia, Spain, May 30–June 3, 2020. pp. 860–873.
8. Hussain AJ, Al-Fayadh A, Radi N. Image compression techniques: a survey in lossless and lossy algorithms. Neurocomputing. 2018; 300: 44–69.
9. Rabie T, Baziyad M, Kamel I. Enhanced high capacity image steganography using discrete wavelet transform and the Laplacian pyramid. Multimedia Tools Appl. 2018; 77 (18): 23673–23698.
10. Tan L, Zeng Y, Zhang W. Research on image compression coding technology. J Phys Conf Ser. 2019; 1284 (1): 012069.
11. Hurley C, Mclean J. Wavelet: Analysis and Methods. Waltham, UK: ED-Tech Press; 2018.
12. Yadav P. A brief description of wavelet and wavelet transforms and their applications. Int J Stat Appl Math. 2018; 3 (1): 266–271.
13. Polat C, Özerdem MS. Introduction to wavelets and their applications in signal denoising. Bitlis Eren Univ J Sci Technol. 2018; 8 (1): 1–10.
14. Amin HU, Yusoff MZ, Ahmad RF. A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomed Signal Process Control. 2020; 56: 101707.
15. Krishnamurthi R, Gopinathan D. Wavelet transformation and machine learning techniques for digital signal analysis in IoT systems. In: Tanwar S, Nayyar A, Rameshwar R. Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead. Boca Raton, FL, USA: CRC Press; 2021. pp. 339–359.
16. Wani NS, Singh RP, Nemade MU. Detection, classification and localization of faults of transmission lines using wavelet transform and neural network. Int J Appl Eng Res. 2018; 13 (1): 98–106.
17. Fashi AA, Samiei MHV, Teixeira A. Design of a visible light photonic chip for Haar transform based optical compression. Optik. 2020; 217: 164929.
18. Monika R, Hemalatha R, Radha S. Energy efficient surveillance system using WVSN with reweighted sampling in modified fast Haar wavelet transform domain. Multimedia Tools Appl. 2018; 77 (23): 30187–30203.
19. Narayana PS, Khan AM. MRI image compression using multiple wavelets at different levels of discrete wavelets transform. J Phys Conf Ser. 2020; 1427 (1): 012002.
20. Brahimi T, Khelifi F, Kacha A. An efficient JPEG-2000 based multimodal compression scheme. Multimedia Tools Appl. 2021; 80 (14): 21241–21260.
21. Khandwani FI, Ajmire PE. A survey of lossless image compression techniques. Int J Electr Electron Computer Sci Eng. 2018; 5 (1): 39–42.
22. Moffat A. Huffman coding. ACM Comput Surveys (CSUR). 2019; 52 (4): 1–35.
23. Liu X, An P, Chen Y, Huang X. An improved lossless image compression algorithm based on Huffman coding. Multimedia Tools Appl. 2022; 81 (4): 4781–4795.
24. Tian J, Rivera C, Di S, Chen J, Liang X, Tao D, Cappello F . Revisiting Huffman coding: toward extreme performance on modern GPU architectures. In: 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, OR, USA, May 17–21, 2021. pp. 881–891.
25. Razaq U, Lizhong X, Li C, Usman M. Evolution and advancement of arithmetic coding over four decades. Open J Sci Technol. 2020; 3 (3): 194–236.
26. Rahman M, Hamada M. Lossless image compression techniques: a state-of-the-art survey. Symmetry. 2919; 11 (10): 1274.
27. Rajan PVS, Fred AL. An efficient compound image compression using optimal discrete wavelet transform and run length encoding techniques. J Intell Syst. 2019; 28 (1): 87–101.
28. Gopinath A, Ravisankar M. Comparison of lossless data compression techniques. In: 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, February 26–28, 2020. pp. 628–633.
29. Gupta N, Vijay R, Gupta HK. Performance evaluation of symmetrical encryption algorithms with wavelet based compression technique. EAI Endorsed Trans Scalable Inform Syst. 2020; 7 (28): e8.
30. Ahmed MA, Aljumah A, Gulam Ahmad MG. Design and implementation of a direct memory access controller for embedded applications. Int J Technol. 2019; 10 (2): 309–319.
31. Yin T, Xu C, Lin L, Jing K . A SiC MOSFET and Si IGBT hybrid modular multilevel converter with specialized modulation scheme. IEEE Trans Power Electron. 2020; 35 (12): 12623–12628.