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- Title
Light Field Hyper Spectral Lossless Compression Employing Greedy Discrete Wavelet and Poincare Recurrence Network.
- Authors
P., Anjaneya; G. K., Rajini
- Abstract
Light field imaging, which can gather global visual data, has garnered attention from the computer world in recent years. Inter and intra frame correlation, big data storage, and poor reconstructed depth quality still plague light field area. In this work, greedy discrete wavelet and poincare recurrence network (GDW-PRN) is proposed for robust lossless image compression of hyper spectral images. First, spatial preprocessing is carried out using discrete reduction wavelet transform to remove unwanted artifacts. Next, dimensionality reduced visual data representation is applied by means of greedy band to the pre-processed images, therefore addressing the issues related to large data storage. Finally, lossless image compression is performed by utilizing poincare recurrence network. The entire proposed work was carried out on light field hyperspectral imagery data using MATLAB simulation tool. The proposed work has been compared to the existing methods such as compression based on differential pulse code modulation (C-DPCM), and 3D wavelet transform and spectrum learning with regression vector (3DWT-SRV). On comparison the acquired findings demonstrate that the suggested GDW-PRN process has high compression proportion light field image storage performance as well as leading to high peak signal to noise ratio (PSNR).
- Subjects
IMAGE compression; PULSE-code modulation; DISCRETE wavelet transforms; LOSSLESS data compression; SIGNAL-to-noise ratio; DATA compression; DATA warehousing; WAVELET transforms
- Publication
International Journal of Intelligent Engineering & Systems, 2023, Vol 16, Issue 5, p386
- ISSN
2185-310X
- Publication type
Article
- DOI
10.22266/ijies2023.1031.33