TY - GEN
T1 - A Multispectral Image Compression Algorithm for Small Satellites Based on Wavelet Subband Coding
AU - Telles, Joel
AU - Kemper, Guillermo
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This article proposes a lossy compression algorithm and scalable multispectral image coding—including blue, green, red, and near-infrared wavelengths—aimed at increasing image quality based on the amount of data received. The algorithm is based on wavelet subband coding and quantization, predictive multispectral image coding at different wavelengths, and the Huffman coding. The methodology was selected due to small satellites’ low data rate and a brief line of sight to earth stations. The test image database was made from the PeruSat-1 and LANDSAT 8 satellites in order to have different spatial resolutions. The proposed method was compared with the SPIHT, EZW, and STW techniques and subsequently submitted to a peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation; it showed better efficiency and reached compression ratios of 20, with a PSNR of 30 and an SSIM of approximately 0.8, depending on the multispectral image wavelength.
AB - This article proposes a lossy compression algorithm and scalable multispectral image coding—including blue, green, red, and near-infrared wavelengths—aimed at increasing image quality based on the amount of data received. The algorithm is based on wavelet subband coding and quantization, predictive multispectral image coding at different wavelengths, and the Huffman coding. The methodology was selected due to small satellites’ low data rate and a brief line of sight to earth stations. The test image database was made from the PeruSat-1 and LANDSAT 8 satellites in order to have different spatial resolutions. The proposed method was compared with the SPIHT, EZW, and STW techniques and subsequently submitted to a peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation; it showed better efficiency and reached compression ratios of 20, with a PSNR of 30 and an SSIM of approximately 0.8, depending on the multispectral image wavelength.
KW - Entropy coding
KW - Image compression
KW - Small satellites
KW - Wavelet transform
UR - https://www.scopus.com/pages/publications/85098123793
U2 - 10.1007/978-3-030-57548-9_17
DO - 10.1007/978-3-030-57548-9_17
M3 - Contribución a la conferencia
AN - SCOPUS:85098123793
SN - 9783030575472
T3 - Smart Innovation, Systems and Technologies
SP - 181
EP - 191
BT - Proceedings of the 5th Brazilian Technology Symposium - Emerging Trends, Issues, and Challenges in the Brazilian Technology
A2 - Iano, Yuzo
A2 - Arthur, Rangel
A2 - Saotome, Osamu
A2 - Kemper, Guillermo
A2 - Padilha França, Reinaldo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th Brazilian Technology Symposium, BTSym 2019
Y2 - 22 October 2019 through 24 October 2019
ER -