A finite mixture model of geometric distributions for lossless image compression

Masmoudi, Afif; Chaoui, Slim

doi:10.1007/s11760-015-0793-1

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…It is a binary map with the same size of the original image, which is filled as follows: label Map(u, v) is set to 1 for the overflow pixels and 0 otherwise. It is also compressed by using arithmetic coding [24,[29][30][31] and then inserted into the mapped prediction error image as an auxiliary information. It is worth mentioning that for most of the tested images, this label map contains zeros only, and consequently, only one bit is required to represent it.…”

Section: (E) Time Complexitymentioning

confidence: 99%

High-Capacity Reversible Data Hiding in Encrypted Images Based on Pixel Prediction and QuadTree Decomposition

Alqahtani,

Masmoudi

2023

Applied Sciences

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Over the past few years, a considerable number of researchers have shown great interest in reversible data hiding for encrypted images (RDHEI). One popular category among various RDHEI methods is the reserving room before encryption (RRBE) approach, which leverages data redundancy in the original image before encryption to create space for data hiding and to achieve high embedding rates (ERs). This paper introduces an RRBE-based RDHEI method that employs pixel prediction, quadtree decomposition, and bit plane reordering to provide high embedding capacity and error-free reversibility. Initially, the content owner predicts the error image using a prediction method, followed by mapping it to a new error image with positive pixel values and a compressed binary label map is generated for overhead pixels. Subsequently, quadtree decomposition is applied to each bit plane of the mapped prediction error image to identify homogeneous blocks, which are then reordered to create room for data embedding. After generating the encrypted image with the encryption key, the data hider employs the data hiding key to embed the data based on the auxiliary information added to each embeddable bit plane’s beginning. Finally, the receiver is able to retrieve the secret message without any error, decrypt the image, and restore it without any loss or distortion. The experimental results demonstrate that the proposed RDHEI method achieves significantly higher ERs than previous competitors, with an average ER exceeding 3.6 bpp on the BOSSbase and BOWS-2 datasets.

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Section: (E) Time Complexitymentioning

confidence: 99%

High-Capacity Reversible Data Hiding in Encrypted Images Based on Pixel Prediction and QuadTree Decomposition

Alqahtani,

Masmoudi

2023

Applied Sciences

Self Cite

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“…A common optimization algorithm is the Expectation-Maximisation (EM) [14]. For example, these distributions can be used in image processing to improve compression [6], for perceptual hashing systems [15], or even automatic image thresholding [16].…”

Section: Curvature Distributionmentioning

confidence: 99%

“…But the curvature is computed locally, while it is often necessary to characterize the shape globally. To do this, we can construct curvature distributions [5,6] and analyze them.…”

Section: Introductionmentioning

confidence: 99%

Analysis of digitized 3D mesh curvature histograms for reverse engineering

Gauthier

Puech

Bénière³

et al. 2017

Computers in Industry

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Today, it has become more frequent and reasonably easy to digitize the surface of 3D objects. However, the obtained results are often inaccurate and noisy. In this paper, we present an efficient method to analyze a curvature histogram from a digitized 3D surface using a real object. Moreover, we propose to use the curvature histogram analysis for many steps of a reverse engineering process, which can be used to retrieve a CAD model from a digitized one for example. Our objective is to design a fast and fully automated method, which is seldom seen in reverse engineering. Experimental results applied on digitized 3D meshes show the efficiency and the robustness of our proposed method.

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“…A common optimization algorithm is the Expectation-Maximisation (EM) [11]. For example, these distributions can be used in image processing to improve compression [10]. Some methods in 3D mesh processing also begin to use distributions, for example to apply segmentation [4] or extract object edges [6].…”

Section: Distributionsmentioning

confidence: 99%

“…But curvatures are locally computed, while it is often necessary to characterize the shape globally. To do this, we can construct curvature distributions [4,10] and analyze them.…”

Section: Introductionmentioning

confidence: 99%

Digitized 3D mesh segmentation based on curvature analysis

Gauthier¹,

Puech²,

Bénière³

et al. 2017

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To cite this version:Silvère AbstractToday, it is increasingly frequent and easy to digitize the surface of real 3D objects. However, the obtained meshes are often inaccurate and noisy. In this paper, we present a method to segment a digitized 3D surface from a real object by analyzing its curvature. Experimental results -applied on real digitized 3D meshes -show the efficiency of our proposed analysis, particularly in a reverse engineering process.

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A finite mixture model of geometric distributions for lossless image compression

Cited by 4 publications

References 14 publications

High-Capacity Reversible Data Hiding in Encrypted Images Based on Pixel Prediction and QuadTree Decomposition

High-Capacity Reversible Data Hiding in Encrypted Images Based on Pixel Prediction and QuadTree Decomposition

Analysis of digitized 3D mesh curvature histograms for reverse engineering

Digitized 3D mesh segmentation based on curvature analysis

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