TITLE

FPGA Based Hyperspectral Image Compression Using DWT and DCT

AUTHOR(S)
Kala, S.; Vasuki, S.
PUB. DATE
May 2014
SOURCE
Australian Journal of Basic & Applied Sciences;May2014, Vol. 8 Issue 7, p81
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Hyperspectral imaging (HSI) is typically defined as a spectral sensing technique which takes hundreds of contiguous narrow waveband images in the visible and infrared regions of the electro-magnetic spectrum. HSI compression is an important issue in remote sensing application. In this paper, an efficient technique for compressing the hyperspectral image is introduced. The proposed HSI compression is based on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). DWT has multi-resolution transformation and DCT has high energy compaction property and requires less computational resources. The idea behind the proposed approach is to apply the DCT to the DWT coefficients of the Hyperspectral images to utilize the advantages of both spatial and spectral redundancies. JPEG is used to perform quantization and encoding of core tensors. The proposed approach has been implemented on Xilinx 14.2 FPGA and tested on real hyperspectral image. The experiments are conducted with HSI compression based on DWT, DCT and JPEG. Compression ratio and PSNR are compared with the existing M-CALIC and the proposed DWT-DCT-JPEG. The result shows that the DWT-DCT-JPEG performs good in terms of compression ratio, PSNR, MSE and memory consumption. Objective: In this paper, an efficient technique for compressing the hyperspectral image is introduced. Results: The HSI-1 compression steps of Paris image use the HSI compression method based on DWT-DCT-JPEG and are shown in Fig.3. The input HSI-1 is loaded and it is split into 7 spectral bands. There are 5 HSI images which are compressed using the proposed technique. The HSI images include Landsat images (.lan) of Paris, Little Colorado River, Mississippi River, Montana state and Rio city. Fig.3. shows the compression steps performed on the landsat image of Paris. Conclusion: The proposed compression method DWT-DCT-JPEG reduces the size of the 3D tensors, which are calculated from the 4 sub-images of the spectral bands of HSI. The simulation experiments are tested on 5 HSI images such as Paris city, Little Colorado River, Mississippi River, Montana State and Rio City with the HSI compression based on DWT, DCT and JPEG. The proposed method is compared with some of the existing compression algorithms like JPEG-LS, M-CALIC, JPEG2000, MJPEG2000, OB-SPECK and DWT-TD (ALS) -RLE. Our proposed work results good in terms of compression ratio, PSNR, MSE and memory consumption.Our future plan is to perform hyperspectral image compression using various encoding techniques with DWT features.
ACCESSION #
96583855

 

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