Lossless, low-complexity, compression of three-dimensional volumetric medical images via linear prediction

Pizzolante, Raffaele; Carpentieri, Bruno

doi:10.1109/icdsp.2013.6622763

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…In the first and the third scenarios, the proposed predictive structure cannot perform the prediction. In these scenarios, it is desirable to use another low-complexity predictive structure and we have used the 3-D Distances-based Linearized Median Predictor (3D-DLMP) [21].…”

Section: Resultsmentioning

confidence: 99%

“…The 2-D Linearized Median Predictor (2D-LMP) [21] uses a prediction context that is composed by three neighboring pixels of , namely, , , and , as shown in Figure 1. In particular, the predictive structure is derived from the well-established 2-D Median Predictor, which is used in JPEG-LS [22].…”

Section: Review Of the 2-d Linearized Median Predictor (2d-lmp)mentioning

confidence: 99%

“…All the pixels that belong to the first band are predicted by using a bi-dimensional predictive structure: the 2-D Linearized Median Predictor (2-D LMP) [21], which exploits only the intra-band correlation, since the first band has no reference bands. The other pixels are predicted by using a new three-dimensional predictive approach, which uses a prediction context composed of the neighboring pixels of X and its reference pixels in the previous bands.…”

Section: Lossless Multiband Compression For Hyperspectral Images (Lmbhi)mentioning

confidence: 99%

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Multiband and Lossless Compression of Hyperspectral Images

Pizzolante

Carpentieri

2016

Algorithms

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Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.

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Section: Resultsmentioning

confidence: 99%

Section: Review Of the 2-d Linearized Median Predictor (2d-lmp)mentioning

confidence: 99%

Section: Lossless Multiband Compression For Hyperspectral Images (Lmbhi)mentioning

confidence: 99%

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Multiband and Lossless Compression of Hyperspectral Images

Pizzolante

Carpentieri

2016

Algorithms

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“…Pixels are individually encoded by representing the prediction error ERR ( ), the difference between the pixel being encoded, PIX ( ) and its predicted luminosity. In [7] author proposed an algorithm as MILC which takes slices of image as input. It uses inter-slice-predictive model to predict the value of next pixel fallowed by error coding.…”

Section: Related Workmentioning

confidence: 99%

Low-Complexity, Lossless Volumetric Medical Image Compression with PPM-5& Linear Prediction

Thombare¹

2016

IJIRCCE

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ABSTRACT:Several of today's imaging techniques such as CT scan, MRI produces three dimensional data sets obtained by generating multiple slices in a single examination, with each slice representing a different cross section of the body part being imaged.These 3-D volumetric medical images are an important source of digital data that need lossless compression to be stored or transmitted. Several improvements have been made in lossless image compression field. These methods can be roughly divided into single pass & multiple pass techniques. Proposed system uses a low complexity, lossless, compression algorithm for the compression of 3-D volumetric medical images that exploits the three-dimensional nature of the data by using 3-D linear prediction.

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“…Each pixel of the input hyperspectral image HI is predicted by using one of the following predictive structures: the 2-D linearized median predictor (2-D LMP) [15] and the 3-D multiband linear predictor (3D-MBLP).…”

Section: Multiband Lossless Compression Of Hyperspectral Imagesmentioning

confidence: 99%

Hyperspectral Data: Efficient and Secure Transmission

Pizzolante

Carpentieri

2017

Algorithms

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Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube): it can be considered as a sequence of narrow and contiguous spectral channels (bands). The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver.

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Lossless, low-complexity, compression of three-dimensional volumetric medical images via linear prediction

Cited by 18 publications

References 14 publications

Multiband and Lossless Compression of Hyperspectral Images

Multiband and Lossless Compression of Hyperspectral Images

Low-Complexity, Lossless Volumetric Medical Image Compression with PPM-5& Linear Prediction

Hyperspectral Data: Efficient and Secure Transmission

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