Prediction of Quality in DCT-Based Lossy Compression of Noisy Remote Sensing Images
| Autor: | A. Zemliachenko, Benoit Vozel, Kacem Chehdi, Vladimir V. Lukin, Sergey K. Abramov |
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| Přispěvatelé: | Università degli Studi di Trento (UNITN), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Nantes Université (NU)-Université de Rennes 1 (UR1) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
noise
Gaussian 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Data_CODINGANDINFORMATIONTHEORY Lossy compression Standard deviation symbols.namesake [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] lossy compression Computer Science::Multimedia 0202 electrical engineering electronic engineering information engineering Discrete cosine transform Performance prediction image Quantization (image processing) 021101 geological & geomatics engineering Mathematics Remote sensing Pixel Noise measurement 020206 networking & telecommunications metric prediction [SPI.TRON]Engineering Sciences [physics]/Electronics Computer Science::Computer Vision and Pattern Recognition symbols |
| Zdroj: | 2017 IEEE 37th International Conference On Electronics and Nanotechnology (elnano) 2017 IEEE 37th International Conference On Electronics and Nanotechnology (elnano), Apr 2017, Kyiv, Ukraine. pp.447-450, ⟨10.1109/ELNANO.2017.7939794⟩ |
| DOI: | 10.1109/ELNANO.2017.7939794⟩ |
| Popis: | International audience; This paper considers specific aspects of lossy compression of noisy remote sensing images. A method based on discrete cosine transform (DCT) in 32x32 pixels blocks is analyzed. Characteristics of noise assumed additive (in original data or after proper variance stabilizing transform), spatially uncorrelated and Gaussian are assumed a priori known. They are taken into consideration in setting quantization step (QS) that is supposed proportional to noise standard deviation. It is demonstrated that statistics of DCT coefficients determined in 8x8 pixel blocks can be employed for prediction of peak signal-to-noise ratio (PSNR) and improvement (or reduction) of visual quality metric PSNR-HVS-M. Approximating curves are obtained by their regression into scatter-plots using low order polynomials. Coder performance prediction can be then used for setting its parameter (QS) for providing appropriate quality of compressed image. Applicability of the proposed prediction approach is proven by experiments with real-life images. |
| Databáze: | OpenAIRE |
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