A Peano-Hilbert derived algorithm for compression of angiocardiographic images

Pinciroli, Francesco; Combi, Carlo; Pozzi, Giuseppe; Negretto, M.; Portoni, L.; Invernizzi, G.

doi:10.1109/cic.1991.169050

Cited by 7 publications

(13 citation statements)

References 5 publications

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“…Such curves have interesting properties which have made them a popular tool in different domains such as querying multi-dimensional data and image compression [12,16]. It is important, however, to note that we are not claiming that space lling curves are the best possible encoders.…”

Section: Space Encodingmentioning

confidence: 99%

Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy

Khoshgozaran

Shahabi

Advances in Spatial and Temporal Databases

223

184

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Abstract. In this paper we propose a fundamental approach to perform the class of Nearest Neighbor (NN) queries, the core class of queries used in many of the location-based services, without revealing the origin of the query in order to preserve the privacy of this information. The idea behind our approach is to utilize one-way transformations to map the space of all static and dynamic objects to another space and resolve the query blindly in the transformed space. However, in order to become a viable approach, the transformation used should be able

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Section: Space Encodingmentioning

confidence: 99%

Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy

Khoshgozaran

Shahabi

Advances in Spatial and Temporal Databases

223

184

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“…Image irreversible compression techniques described in literature are several; we mention here Vector Quantization [4] and the image compression standard JPEG [5]. In the past we proposed two different irreversible compression techniques for angiocardiographic images: the BEL (Brightness Error Limitation) method and the PABEL (Pseudo-gradient Adaptive Brightness Error Limitation) method [6]. Basic principle of those methods is to limit maximum difference of brightness between corresponding pixels of the original image and of the reconstructed one.…”

Section: Compression Techniquesmentioning

confidence: 99%

“…This new method is somehow a evolution of PABEL method [6] and its basic principle is not to limit, in a adaptive way, just the brightness error but also the contrast error observed considering consecutive corresponding pixels in the original image and in the reconstructed one, according to the PeanoHilbert scan path. In this way we aimed to maintain unchanged the contrast present between pixels of the original image, in areas with a stronger contrast, like vessels contours.…”

Section: Pabcel (Pseudo-gradient Adaptive Brightness Andmentioning

confidence: 99%

“…To scan images we implemented a scan algorithm based on tlie Peano-Hilbert plane-filling curve [6]. The Italian mathematician and logician G. Peano in 1890 showed how a single point, moving continuously over a square, could (in a finite time) pass at least once through every point on the square and its boundary.…”

Section: Image Scan According To Peanohi1 Be R T Pathmentioning

confidence: 99%

“…To do that, we implemented an adaptive reversible compression method based on Lernpel-Ziv-Welch's algorithm [6].…”

Section: Encoding Techniquementioning

confidence: 99%

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Effective data compression of angiocardiographic static images with PABCEL method

Pinciroli

Combi²,

Pozzi³

et al.

Proceedings Computers in Cardiology

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We implemented and applied a new compression method, called Pseudo-gradient Adaptive Brightness and Contrast Error Limitation (PABCEL) method, on singleimages coming from angiocardiographic f lms, recorded and digitized at the Haemodynanric Labs of the Cardiology Division of the Ospedali Riuniti, Bergamo. PABCEL algoritlim a i m at liinitirig maximum difference of brightness between corresponding pixels of the original image and the reconstructed one; it also limits the contrast error when considering corresponding pixels of the original and the reconstructed images on the Peano-Hilbert scan path. We applied our compression method to 168 images coming from 14 different angiocardiographic exams; with the lowest threshold values we obtained a mean conipressiori ratio of about 8:l: consulted cardiologists didn't identify significant diferences between original irriages and reconstructed ones. Qualitative and quantitative evaluation of obtained results has been carried out.

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Effect of d-Dimensional Re-orderings on Lossless Compression of Radio-Astronomy and Digital Elevation Data

2021

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For multidimensional data, Space-Filling Curves (SFCs) have been used to improve the execution time of spatial data queries. However, their effect on compression, when used to reorder the uncompressed values, is known to a lesser extent. We investigate the impact of three SFCs on Shuttle Radar Topographic Mission (SRTM) elevation data and Square-Kilometre Array telescope (SKA) radio-astronomy data: two types of datasets to which SFCs have not been extensively applied, within a compression context. This work contributes to the understanding of how such reorderings impact compression performance and affect different compression schemes and preprocessing techniques through their use. We show empirical results from combining eight common compression schemes, the Z-Order, Gray-Code, and Hilbert spacefilling curves, and the bitwise preprocessing technique BitShuffle. The Hilbert Curve consistently outperforms the other orderings for the SRTM dataset though the mapping implementation incurs a significant speed penalty. However, the Z-Order and Gray-Code Curves are best for the SKA dataset. Through an analysis of the dataset autocorrelations, file-entropies, and block-entropies; we show that the SKA dataset's dimensional bias is not exploited as much by the Hilbert Curve compared to the Z-Order and Gray-Code Curves. However, the Hilbert Curve is the most appropriate for the SRTM dataset as it can be modelled as isotropic and has a significantly higher level of local autocorrelation. BitShuffle is necessary to practically compress the SKA data, but does contribute to the compression performance of the SRTM dataset. These curves and BitShuffle are advantageous in reducing block-entropy values for such datasets.

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A Peano-Hilbert derived algorithm for compression of angiocardiographic images

Cited by 7 publications

References 5 publications

Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy

Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy

Effective data compression of angiocardiographic static images with PABCEL method

Effect of d-Dimensional Re-orderings on Lossless Compression of Radio-Astronomy and Digital Elevation Data

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