Regression Wavelet Analysis for Near-Lossless Remote Sensing Data Compression

Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.

[1]  Ali Can Karaca,et al.  Lossless hyperspectral image compression using bimodal conventional recursive least-squares , 2018 .

[2]  Enrico Magli,et al.  Unified Lossy and Near-Lossless Hyperspectral Image Compression Based on JPEG 2000 , 2008, IEEE Geoscience and Remote Sensing Letters.

[3]  Shuxu Guo,et al.  Lossless compression of hyperspectral images using conventional recursive least-squares predictor with adaptive prediction bands , 2016 .

[4]  Tsung-Ching Lin,et al.  A Near Lossless Wavelet-Based Compression Scheme for Satellite Images , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[5]  Michael W. Marcellin,et al.  Unbiasedness of regression wavelet analysis for progressive lossy-to-lossless coding , 2016, 2016 Picture Coding Symposium (PCS).

[6]  Chao Deng,et al.  Lossless compression of hyperspectral imagery using a fast adaptive-length-prediction RLS filter , 2019, Remote Sensing Letters.

[7]  Valero Laparra,et al.  Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Michael W. Marcellin,et al.  Low Complexity Prediction Model for Coding Remote-Sensing Data with Regression Wavelet Analysis , 2017, 2017 Data Compression Conference (DCC).

[9]  Michael W. Marcellin,et al.  Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data , 2016, 2016 Data Compression Conference (DCC).

[10]  Bormin Huang,et al.  Lossless Compression of Hyperspectral Imagery via Clustered Differential Pulse Code Modulation with Removal of Local Spectral Outliers , 2015, IEEE Signal Processing Letters.

[11]  Michael W. Marcellin,et al.  A Lightweight Contextual Arithmetic Coder for On-Board Remote Sensing Data Compression , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Bormin Huang,et al.  Lossless Compression of Hyperspectral Images Using Clustered Linear Prediction With Adaptive Prediction Length , 2012, IEEE Geoscience and Remote Sensing Letters.

[13]  Michael W. Marcellin,et al.  Isorange Pairwise Orthogonal Transform , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[14]  E. Magli,et al.  A Tutorial on Image Compression for Optical Space Imaging Systems , 2014, IEEE Geoscience and Remote Sensing Magazine.

[15]  Joan Bartrina-Rapesta,et al.  Multilevel Split Regression Wavelet Analysis for Lossless Compression of Remote Sensing Data , 2018, IEEE Geoscience and Remote Sensing Letters.

[16]  Ian Blanes,et al.  A Fully Embedded Two-Stage Coder for Hyperspectral Near-Lossless Compression , 2015, IEEE Geoscience and Remote Sensing Letters.

[17]  P. Bao,et al.  Near-lossless image compression by combining wavelets and CALIC , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[18]  Enrico Magli,et al.  Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[19]  Jinwei Song,et al.  Lossless compression of hyperspectral imagery via RLS filter , 2013 .

[20]  Rashid Ansari,et al.  Near-lossless image compression techniques , 1998, Electronic Imaging.

[21]  Enrico Magli,et al.  Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Ian Blanes,et al.  Pairwise Orthogonal Transform for Spectral Image Coding , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Joan Bartrina-Rapesta,et al.  Hyperspectral IASI L 1 C Data Compression , 2017 .

[24]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.

[25]  Joan Serra-Sagristà,et al.  Enumeration of lattice points in l1 norm , 2000, Inf. Process. Lett..

[26]  Naoufal Amrani,et al.  Low complexity regression wavelet analysis variants for hyperspectral data lossless compression , 2018 .

[27]  J. Serra-Sagristà,et al.  Progressive lossy-to-lossless coding of hyperspectral images through regression wavelet analysis , 2018 .

[28]  Ali Bilgin,et al.  Clustering Regression Wavelet Analysis for Lossless Compression of Hyperspectral Imagery , 2019, 2019 Data Compression Conference (DCC).

[29]  Stephen J. Wright,et al.  Nonlinear Least-Squares Problems , 1999 .

[30]  Enrico Magli,et al.  A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  I. Daubechies,et al.  Wavelet Transforms That Map Integers to Integers , 1998 .

[32]  Michael W. Marcellin,et al.  Lossless Image Compression Using Reversible Integer Wavelet Transforms and Convolutional Neural Networks , 2018, 2018 Data Compression Conference.

[33]  Enrico Magli,et al.  Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC , 2004, IEEE Geoscience and Remote Sensing Letters.

[34]  Mitică Craus,et al.  Lossless multispectral and hyperspectral image compression on multicore systems , 2017, 2017 21st International Conference on System Theory, Control and Computing (ICSTCC).

[35]  Åke Björck,et al.  Least Squares Problems , 2009, Encyclopedia of Optimization.