Near-Lossless Compression of Hyperspectral Imagery Through Crisp/Fuzzy Adaptive DPCM

Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano; Lastri, Cinzia; Santurri, Leonardo

doi:10.1007/0-387-28600-4_6

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…One of the techniques to reduce the large amount of data storage is to apply a lossy compression method (Aiazzi et al, 2006;Mielikainen and Toivanen, 2006). "Lossy", means that, once the input image has been encoded and later decoded, the recovered image is not exactly the same as the original input image, but some information has been removed.…”

Section: Remote Sensing Imagesmentioning

confidence: 99%

A novel semi-fragile forensic watermarking scheme for remote sensing images

Serra-Ruiz

Megías

2011

International Journal of Remote Sensing

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A semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote sensing images applying a tree structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of the multispectral or hyperspectral image is used to embed the mark in it order to detect any significant modification of the original image. The image is segmented into threedimensional blocks and a tree structured vector quantizer is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.

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Section: Remote Sensing Imagesmentioning

confidence: 99%

A novel semi-fragile forensic watermarking scheme for remote sensing images

Serra-Ruiz

Megías

2011

International Journal of Remote Sensing

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“…A lossy compression technique [ 22 , 23 ] is used to reduce the amount of data needed to represent the image. The term “lossy” implies that some data from the original image is permanently lost.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain

Serra-Ruiz

Qureshi

Megías

2019

Entropy

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This article presents a semi-fragile image tampering detection method for multi-band images. In the proposed scheme, a mark is embedded into remote sensing images, which have multiple frequential values for each pixel, applying tree-structured vector quantization. The mark is not embedded into each frequency band separately, but all the spectral values (known as signature) are used. The mark is embedded in the signature as a means to detect if the original image has been forged. The image is partitioned into three-dimensional blocks with varying sizes. The size of these blocks and the embedded mark is determined by the entropy of each region. The image blocks contain areas that have similar pixel values and represent smooth regions in multispectral or hyperspectral images. Each block is first transformed using the discrete wavelet transform. Then, a tree-structured vector quantizer (TSVQ) is constructed from the low-frequency region of each block. An iterative algorithm is applied to the generated trees until the resulting tree fulfils a requisite criterion. More precisely, the TSVQ tree that matches a particular value of entropy and provides a near-optimal value according to Shannon's rate-distortion function is selected. The proposed method is shown to be able to preserve the embedded mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their positions in the whole image. Experimental results show how the scheme can be applied to detect forgery attacks, and JPEG2000 compression of the images can be applied without removing the authentication mark. The scheme is also compared to other works in the literature.

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“…information of the data but allows a moderate decrement in transmission rate to be achieved. 12,13 The bottleneck of downlink to ground stations may hamper the coverage capabilities of modern satellite instruments. If strictly lossless techniques are not used, a certain amount of information of the data will be lost.…”

Section: Introductionmentioning

confidence: 99%

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

Aiazzi

Alparone

Baronti

et al. 2013

J. Appl. Remote Sens

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Abstract.A rate-distortion model to measure the impact of near-lossless compression of raw data, that is, compression with user-defined maximum absolute error, on the information available once the compressed data have been received and decompressed is proposed. Such a model requires the original uncompressed raw data and their measured noise variances. Advanced nearlossless methods are exploited only to measure the entropy of the datasets but are not required for on-board compression. In substance, the acquired raw data are regarded as a noisy realization of a noise-free spectral information source. The useful spectral information at the decoder is the mutual information between the unknown ideal source and the decoded source, which is affected by both instrument noise and compression-induced distortion. Experiments on simulated noisy images, in which the noise-free source and the noise realization are exactly known, show the trend of spectral information versus compression distortion, which in turn is related to the coded bit rate or equivalently to the compression ratio through the rate-distortion characteristic of the encoder used on satellite. Preliminary experiments on airborne visible infrared imaging spectrometer (AVIRIS) 2006 Yellowstone sequences match the trends of the simulations. The main conclusion that can be drawn is that the noisier the dataset, the lower the CR that can be tolerated, in order to save a prefixed amount of spectral information. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Near-Lossless Compression of Hyperspectral Imagery Through Crisp/Fuzzy Adaptive DPCM

Cited by 6 publications

References 47 publications

A novel semi-fragile forensic watermarking scheme for remote sensing images

A novel semi-fragile forensic watermarking scheme for remote sensing images

Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

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