Comparing CSI and PCA in Amalgamation with JPEG for Spectral Image Compression
This work addresses the storage and transmission challenges of spectral images for researchers and practitioners, but it is incremental as it builds on prior color image compression research and compares existing techniques.
The paper tackled spectral image compression by comparing cubic spline interpolation (CSI) and principal component analysis (PCA) combined with JPEG, finding that CSI achieved lower complexity and computational efficiency, especially for large images, while maintaining small color differences (dE00) compared to PCA at fixed compression rates.
Continuing our previous research on color image compression, we move towards spectral image compression. This enormous amount of data needs more space to store and more time to transmit. To manage this sheer amount of data, researchers have investigated different techniques so that image quality can be conserved and compressibility can be improved. The principle component analysis (PCA) can be employed to reduce the dimensions of spectral images to achieve high compressibility and performance. Due to processing complexity of PCA, a simple interpolation technique called cubic spline interpolation (CSI) was considered to reduce the dimensionality of spectral domain of spectral images. The CSI and PCA were employed one by one in the spectral domain and were amalgamated with the JPEG, which was employed in spatial domain. Three measures including compression rate (CR), processing time (Tp) and color difference CIEDE2000 were used for performance analysis. Test results showed that for a fixed value of compression rate, CSI based algorithm performed poor in terms of dE00, in comparison with PCA, but is still reliable because of small color difference. On the other hand it has lower complexity and is computationally much better as compared to PCA based algorithm, especially for spectral images with large size.