Lossy DCT-based compression of remote sensing images with providing a desired visual quality

Modern remote sensing (RS) systems produce a huge amount of data that should be passed to potential users from sensors or saved. Then, compression is an operation that is extremely useful where lossy compression has found many applications. A requirement to it is not to loose useful information contained in RS data and to provide a rather high compression ratio (CR). This has to be done in automatic manner and quickly enough. One possible approach to ensure minimal or appropriate loss of useful information is to provide a desired visual quality of compressed images where introduced distortions are invisible. In this paper, we show how this can be done for coders based on discrete cosine transform (DCT) that employ either uniform or non-uniform quantization of DCT coefficients. For multichannel images that contain sub-band images with different dynamic range, it is also proposed to carry out preliminary normalization. Additionally, compression performance can be improved if sub-band images are compressed in groups. Then, either introduced distortions are smaller for a given CR or a larger CR is provided for a given level of compressed data quality. Examples for real-life data are presented.

[1]  Pierre Duhamel,et al.  Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding , 2008, IEEE Transactions on Image Processing.

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

[3]  S. Krivenko,et al.  SMART LOSSY COMPRESSION OF IMAGES BASED ON DISTORTION PREDICTION , 2018 .

[4]  Weisi Lin,et al.  An Overview of Perceptual Processing for Digital Pictures , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[5]  Nikolay N. Ponomarenko,et al.  Automatic Approaches to On-Land/On-Board Filtering and Lossy Compression of AVIRIS Images , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Fouad Khelifi,et al.  Joined Spectral Trees for Scalable SPIHT-Based Multispectral Image Compression , 2008, IEEE Transactions on Multimedia.

[7]  Vladimir V. Lukin,et al.  Lossy Compression of Remote Sensing Images with Controllable Distortions , 2018, Satellite Information Classification and Interpretation.

[8]  Mikhail Zriakhov,et al.  Lossy Compression of Images with Additive Noise , 2005, ACIVS.

[9]  Arto Kaarna,et al.  Compression of Spectral Images , 2007 .

[10]  Luciano Alparone,et al.  Spectral Distortion in Lossy Compression of Hyperspectral Data , 2012, J. Electr. Comput. Eng..

[11]  Nikolay N. Ponomarenko,et al.  Estimation of accessible quality in noisy image compression , 2006, 2006 14th European Signal Processing Conference.

[12]  Martin Sweeting,et al.  Image compression systems on board satellites , 2009 .

[13]  Giovanni Motta,et al.  Handbook of Data Compression , 2009 .

[14]  Robert A. Schowengerdt,et al.  Remote Sensing, Third Edition: Models and Methods for Image Processing , 2006 .

[15]  Vladimir V. Lukin,et al.  Image informative maps for component-wise estimating parameters of signal-dependent noise , 2013, J. Electronic Imaging.

[16]  Nikolay N. Ponomarenko,et al.  Processing of Hyperspectral Imagery for Contamination Detection in Urban Areas , 2011 .

[17]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[18]  J. Astola,et al.  ON BETWEEN-COEFFICIENT CONTRAST MASKING OF DCT BASIS FUNCTIONS , 2007 .

[19]  Vladimir V. Lukin,et al.  Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images , 2012 .

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

[21]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[22]  Nikolay N. Ponomarenko,et al.  Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform , 2014 .

[23]  Russell M. Mersereau,et al.  Lossy compression of noisy images , 1998, IEEE Trans. Image Process..

[24]  Nikolay N. Ponomarenko,et al.  Analysis of HVS-Metrics' Properties Using Color Image Database TID2013 , 2015, ACIVS.

[25]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[26]  Joseph Meola,et al.  Modeling and estimation of signal-dependent noise in hyperspectral imagery. , 2011, Applied optics.

[27]  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.

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

[29]  Vladimir V. Lukin,et al.  Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters , 2015 .

[30]  Nikolay N. Ponomarenko,et al.  DCT Based High Quality Image Compression , 2005, SCIA.

[31]  Nikolay N. Ponomarenko,et al.  Still image/video frame lossy compression providing a desired visual quality , 2015, Multidimensional Systems and Signal Processing.

[32]  Emmanuel Christophe.  Hyperspectral Data Compression Tradeoff , 2011 .

[33]  F. Windmeijer,et al.  An R-squared measure of goodness of fit for some common nonlinear regression models , 1997 .

[34]  Antonio J. Plaza,et al.  On the Impact of Lossy Compression on Hyperspectral Image Classification and Unmixing , 2011, IEEE Geoscience and Remote Sensing Letters.