Authors : Sujendra G Bharadwaj; Shruthi B

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/ycxdy7k7

Scribd : https://tinyurl.com/42mprjv7

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT1859

Abstract : This paper delves into the considerable challenges of working with hyperspectral images, which are notably large and multidimensional, with file sizes often surpassing hundreds of megabytes. Hyperspectral imaging captures light across a continuous range of wavelengths, providing detailed spectral information for each pixel. This rich dataset is invaluable for applications such as environmental monitoring, precision agriculture, mineral exploration, and medical diagnostics, where accurate spectral data aids in identifying materials and detecting subtle variations. However, the immense data volume not only strains storage and transmission resources but also requires efficient processing and analysistechniques to handle the high-dimensional data without compromising quality. Additionally, compression methods are essential to manage storage constraints and improve real-time usability, but they must balance data reduction with the preservation of spectral integrity for effective analysis and application.

Keywords : JPEG Compression, Discrete Cosine Transform, Quantization, Image Decompression.

References :

1. Soni, H., & Gupta, S. (2020). "A Study on Lossy Image Compression Techniques." International Journal of Computer Applications.
2. Sayood, K. (2020). Introduction to Data Compression. Morgan Kaufmann.
3. Jain, A. K. (2021). "Image Compression Techniques: A Comprehensive Review." International Journal of Image and Graphics.
4. Jia, C., Wu, H., & Wang, L. (2021). "Efficient JPEG Image Compression Method Based on Optimized Huffman Coding." Journal of Information Science, 47(2), 238-250.
5. Zhang, L., Liu, Z., & Wang, X. (2022). "Adaptive Huffman Coding for Image Compression." Applied Sciences, 12(4), 1804.
6. Feng, L., Zhang, H., & Zhao, J. (2022). "Color Image Processing Using YCbCr Color Space." Journal of Visual Communication and Image Representation.
7. Khan, A., & Kumar, P. (2022). "Entropy Coding Techniques in Image Compression." Journal of Computer Science and Technology.
8. Zhang, J., Wang, L., & Zhou, Y. (2022). "Efficient DCT Algorithms for Image Compression." Journal of Real- Time Image Processing, 19(3), 605-617.
9. Wang, C., Zhou, X., & Liu, Y. (2023). "An Overview of Image Compression Algorithms." Applied Sciences.
10. Huang, H., & Yao, X. (2023). "A Fast Huffman Coding Algorithm for Image Compression." Journal of Visual Communication and Image Representation, 104, 103338.
11. Lee, S., Kim, J., & Cho, H. (2023). "Improving JPEG Compression Efficiency Using Enhanced Huffman Coding Techniques." IEEE Transactions on Image Processing, 32, 1872-1883.
12. Singh, R., Kumar, A., & Gupta, S. (2023). "Adaptive Discrete Cosine Transform for Image Compression." International Journal of Imaging Systems and Technology, 33(1), 234-244.
13. Chen, Y., Liu, X., & Wang, H. (2023). "Perceptual Quantization for Image Compression Based on Human Visual Sensitivity." IEEE Transactions on Image Processing, 32(4), 1020-1034.
14. Wang, R., Zhang, Y., & Liu, Z. (2023). "Adaptive Huffman Coding for Enhanced JPEG Compression." Journal of Visual Communication and Image Representation, 88, 103271.
15. Bhatia, S., Singh, A., & Sahu, S. (2023). "Performance Analysis of Arithmetic Coding in JPEG Compression." Journal of Electronic Imaging, 32(1), 013001.
16. Zhao, Y., Liu, D., & Zhang, X. (2023). "Parallel Processing Techniques for Fast JPEG Decompression." Journal of Computer and System Sciences, 131, 105-116.
17. Khan, M. A., Ahmad, J., & Alam, M. (2023). "Machine Learning-Based Image Reconstruction for JPEG Decompression." Pattern Recognition Letters, 168, 25-32.
18. Jayalakshmi, B., Satish, B. K., & Rajan, V. K. (2023). "A Comprehensive Review on Image Compression Techniques." Journal of Ambient Intelligence and Humanized Computing.
19. Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing. Pearson.
20. Salami, M. et al. (2018). "Image Compression: An Overview." Journal of Computing and Security.
21. Bhandari, M., Sharma, P., & Mehta, A. (2022). "Application of Machine Learning in Image Compression Techniques: A Survey." International Journal of Image and Graphics.
22. Li, P., Wu, Y., & Zhang, Y. (2024). "Comparative Analysis of AVIF and JPEG for Image Compression." Journal of Visual Communication and Image Representation.