A hybrid gray image representation using spatial- and DCT-based approach with application to moment computation

Chung, Kuo–Liang; Liu, Yau-Wen; Yan, Wen-Ming

doi:10.1016/j.jvcir.2006.01.002

Cited by 28 publications

(12 citation statements)

References 25 publications

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“…First, to corroborate the efficiency of our proposed SCSA method for image representation, Table 1 shows the experimental results obtained from the comparison between the SCSA method and the S-Tree Coding (STC) [4] and the Overlapping Rectangular Non-symmetry and Anti-packing Model (ORNAM) [23] each one of size 512 × 512 (see Fig. 1) together with the peak signal-to-noise ratio (PSNR) are used to show the performance of the proposed method.…”

Section: Numerical Results and Discussionmentioning

confidence: 95%

“…Let V 2 be a positive real valued C ∞ function on 2 considered as potential of the Schrödinger operator (3). Then, for any pair [11]) (4) and, uniformly for (x, y) ∈ 2 , we have…”

Section: Extension Of the Scsa Methods To Two-dimensionmentioning

confidence: 99%

“…In Section 4, an algorithm for image representation, based on the spectral problems of 1D Schrödinger operators is introduced. The analysis of some parameters and the use of this algorithm in image representation is illustrated in Section 5, followed by a comparison with algorithms from state-of-the-art image representation methods [4,23]. The last Section summarizes and discusses the obtained results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

Kaisserli¹,

Laleg‐Kirati²,

Lahmar-Benbernou³

2015

Digital Signal Processing

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Section: Numerical Results and Discussionmentioning

confidence: 95%

“…Let V 2 be a positive real valued C ∞ function on 2 considered as potential of the Schrödinger operator (3). Then, for any pair [11]) (4) and, uniformly for (x, y) ∈ 2 , we have…”

Section: Extension Of the Scsa Methods To Two-dimensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

Kaisserli¹,

Laleg‐Kirati²,

Lahmar-Benbernou³

2015

Digital Signal Processing

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“…x p for a specific p, where 0£p£4, can be computed in O(1) time by using the following five separated equations [8,11].…”

Section: Theoremmentioning

confidence: 99%

“…Representing and manipulating images are two important issues in the fields of computer graphics, image processing, and pattern recognition [10][11][12]. Computing moments on the image is very important in the field of image processing [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Zheng

Sarem

2010

Front. Comput. Sci. China

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Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model (NAM) is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding (STC) representation and by using the NAM and extended shading (NAMES) approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O(N) time where N is the number of NAM blocks. By taking three idiomatic standard gray images 'Lena', 'F16', and 'Peppers' in the field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.

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Map algebra on raster datasets represented by compact data structures

2023

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The increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in‐memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called k2$$ {k}^2 $$‐raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. k2$$ {k}^2 $$‐raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in‐memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on k2$$ {k}^2 $$‐raster without a decompression procedure.

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A hybrid gray image representation using spatial- and DCT-based approach with application to moment computation

Cited by 28 publications

References 25 publications

A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Map algebra on raster datasets represented by compact data structures

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