• B.Sucharitha
• Sameera Ather

hyper spectral (HS) image compression approaches achieved significant advances from diverse types of coding standards/approaches. HS image compression requires an unconventional coding technique because of its unique, multiple-dimensional structure. Compression of hyper spectral images is undertaken to reduce the on-board memory requirement, communication channel capacity and the download time. Compression algorithms can be either lossless or lossy. In hyper spectral image compression, wavelets have shown a good adaptability to a wide range of data. Some wavelet-based compression methods have been successfully used for hyper spectral image data. Fractal Image Compression is an approach for better image compression. The main objective of this method is to provide simple and better compression results, which is based on proposed Quad tree Decomposition and Discrete Wavelet Transform method for a hyper spectral image. Fractal image compression can be obtained by dividing the RGB image into un overlapped blocks depending on a quantization value and the well-known techniques of Quad tree decomposition. The experiments are conducted with HSI compression based on DWT, Fractal. The results of our work are found to be good in terms of Compression Ratio (CR) and Peak Signal-to-Noise Ratio (PSNR).

HSI, DWT, CR, PSNR

"Hyper Spectral Image Compression Using Fractal Compression with Arithmetic and Huffman Coding", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.90-95, June 2019, Available :http://www.jetir.org/papers/JETIRDC06016.pdf

"Hyper Spectral Image Compression Using Fractal Compression with Arithmetic and Huffman Coding", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp90-95, June 2019, Available at : http://www.jetir.org/papers/JETIRDC06016.pdf