316 research outputs found

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

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    Peer-reviewedA 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 themmultispectral 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.Se presenta un esquema de marcas de agua semi-frágiles para múltiples imágenes de banda. Proponemos incorporar una marca en imágenes de detección remota, aplicando un enfoque de cuantización del vector de árbol estructurado con las definiciones de píxel, en lugar de procesar cada banda por separado. La firma de la imagen hiperespectral se utiliza para insertar la marca en el mismo orden para detectar cualquier modificación significativa de la imagen original. La imagen es segmentada en bloques tridimensionales y un cuantificador de vector de estructura de árbol se construye para cada bloque. Estos árboles son manipulados utilizando un algoritmo iteractivo hasta que el bloque resultante satisface un criterio necesario que establece la marca incrustada. El método se muestra para poder preservar la marca bajo compresión con pérdida (por encima de un umbral establecido) pero, al mismo tiempo, detecta posiblemente bloques forjados y su posición en la imagen entera.Es presenta un esquema de marques d'aigua semi-fràgils per a múltiples imatges de banda. Proposem incorporar una marca en imatges de detecció remota, aplicant un enfocament de quantització del vector d'arbre estructurat amb les definicions de píxel, en lloc de processar cada banda per separat. La signatura de la imatge hiperespectral s'utilitza per inserir la marca en el mateix ordre per detectar qualsevol modificació significativa de la imatge original. La imatge és segmentada en blocs tridimensionals i un quantificador de vector d'estructura d'arbre es construeix per a cada bloc. Aquests arbres són manipulats utilitzant un algoritme iteractiu fins que el bloc resultant satisfà un criteri necessari que estableix la marca incrustada. El mètode es mostra per poder preservar la marca sota compressió amb pèrdua (per sobre d'un llindar establert) però, al mateix temps, detecta possiblement blocs forjats i la seva posició en la imatge sencera

    Quality criteria benchmark for hyperspectral imagery

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    Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionnally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data.We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data

    A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

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    Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding

    An Overview of Multi-Processor Approximate Message Passing

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    Approximate message passing (AMP) is an algorithmic framework for solving linear inverse problems from noisy measurements, with exciting applications such as reconstructing images, audio, hyper spectral images, and various other signals, including those acquired in compressive signal acquisiton systems. The growing prevalence of big data systems has increased interest in large-scale problems, which may involve huge measurement matrices that are unsuitable for conventional computing systems. To address the challenge of large-scale processing, multiprocessor (MP) versions of AMP have been developed. We provide an overview of two such MP-AMP variants. In row-MP-AMP, each computing node stores a subset of the rows of the matrix and processes corresponding measurements. In column- MP-AMP, each node stores a subset of columns, and is solely responsible for reconstructing a portion of the signal. We will discuss pros and cons of both approaches, summarize recent research results for each, and explain when each one may be a viable approach. Aspects that are highlighted include some recent results on state evolution for both MP-AMP algorithms, and the use of data compression to reduce communication in the MP network

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

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    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 avail- able 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 near- lossless 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 spec- trometer (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. (DOI: 10.1117/1.JRS.

    Constant-SNR, rate control and entropy coding for predictive lossy hyperspectral image compression

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    Predictive lossy compression has been shown to represent a very flexible framework for lossless and lossy onboard compression of multispectral and hyperspectral images with quality and rate control. In this paper, we improve predictive lossy compression in several ways, using a standard issued by the Consultative Committee on Space Data Systems, namely CCSDS-123, as an example of application. First, exploiting the flexibility in the error control process, we propose a constant-signal-to-noise-ratio algorithm that bounds the maximum relative error between each pixel of the reconstructed image and the corresponding pixel of the original image. This is very useful to avoid low-energy areas of the image being affected by large errors. Second, we propose a new rate control algorithm that has very low complexity and provides performance equal to or better than existing work. Third, we investigate several entropy coding schemes that can speed up the hardware implementation of the algorithm and, at the same time, improve coding efficiency. These advances make predictive lossy compression an extremely appealing framework for onboard systems due to its simplicity, flexibility, and coding efficiency

    Compression of Spectral Images

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    Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution

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    In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset
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