Adaptive searchless fractal image compression in DCT domain

Gupta, Ritu; Mehrotra, Deepti; Tyagi, Rajesh Kumar

doi:10.1080/13682199.2016.1219100

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…The DCT is frequently used in image processing. 29,30 The DCT implemented based on the following equation:…”

Section: Dctmentioning

confidence: 99%

“…A DCT represents a series of finitely data points in terms of a sum of oscillating cosine functions at various frequencies. The DCT is frequently used in image processing 29,30 . The DCT implemented based on the following equation:

C false(u, v false) = α false(u false) α false(v false) \sum_{x = 0}^{M - 1} \sum_{y = 0}^{N - 1} f false(x, y false) \cos [\frac{π (2 x goodbreak+ 1) u}{2 M}] \cos [\frac{π (2 y goodbreak+ 1) v}{2 N}] .

…”

Section: Feature Extraction and Fusionmentioning

confidence: 99%

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Robust features fusion utilization for supervised palmprint recognition

Ata

Elgamily²,

Mohamed

2022

Concurrency and Computation

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This article proposes robust features fusion methodology for supervised palmprint recognition. The process of features fusion has been formulated according to the hybridization between robust morphological features of principal lines and the following features extractors: principal component analysis (PCA), transformed domain extractors, and invariant moments. The region of interest (ROI) has been extracted based on the identification of the valley points locations. Moreover, both morphological operations and edge detection have been applied in order to formulate the correct ROI. First, both length and slope of each extracted principal line have been estimated morphologically. Second, different features extraction techniques have been appliedfor the resultant ROI including (PCA), transformed domain, and invariant moments.Finally, seven supervised machine learning methods have been manipulated in order to achieve the highest palmprint recognition accuracy. The accuracy of the recognition was evaluated by measuring sensitivity, dice, precision, Jaccard coefficients, correlation, accuracy, and recognition time. Experimental findings show that, among all tested machine learning methods, the feed forward neural network with back propagation based on features vector of the fusion between invariant moments and principal lines morphology has achieved approximately 99.9% of successful palmprints recognition. K E Y W O R D Sdiscrete cosine transform, discrete wavelet transform, invariant moments, principal component analysis, Hough transform, morphological features, transformed domain.

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“…The DCT is frequently used in image processing. 29,30 The DCT implemented based on the following equation:…”

Section: Dctmentioning

confidence: 99%

C false(u, v false) = α false(u false) α false(v false) \sum_{x = 0}^{M - 1} \sum_{y = 0}^{N - 1} f false(x, y false) \cos [\frac{π (2 x goodbreak+ 1) u}{2 M}] \cos [\frac{π (2 y goodbreak+ 1) v}{2 N}] .

…”

Section: Feature Extraction and Fusionmentioning

confidence: 99%

Robust features fusion utilization for supervised palmprint recognition

Ata

Elgamily²,

Mohamed

2022

Concurrency and Computation

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show abstract

“…The second type (i.e. the feature‐vector‐based methods) [16–21] used or created some features to represent the image blocks. These features were used to sort the image blocks [16], to discard the domain blocks [17, 18, 21], to determine the selection of relevant domain blocks [19], or to use the image complexity as an index for the domain blocks [20].…”

Section: Introductionmentioning

confidence: 99%

Fast fractal image compression algorithm using specific update search

Zheng

Sarem

2020

IET Image Processing

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“…As the encoding time of the partial-search based methods is still high and not always sufficient for high-speed applications, the researchers have also proposed various fractal algorithms based on the searchless approach such as [23], [24]. Although these methods are the fastest among the others, they are suffering from poor reconstruction fidelity [12], [25].…”

Section: Introductionmentioning

confidence: 99%

Impact of Spatial Dynamic Search With Matching Threshold Strategy on Fractal Image Compression Algorithm Performance: Study

et al. 2020

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Fractal image compression (FIC) is a very popular technique in image compression applications due to its simplicity and superior performance. However, it has a major drawback, which is the long encoding time. This is due to the requirement of performing huge similarity search for encoding each small portion in the image. Thus, reducing the search time of FIC while keeping the quality of reconstructed images at acceptable level is still an active research topic. Therefore, this paper has focused on studying the search problem of the conventional full-search FIC algorithm and the impact of employing a spatial dynamic search technique instead with the matching threshold strategy. Unlike the conventional full-search method that is a spatially static where the search starts from a fixed position (normally from the top-left corner of the image to the bottom-right corner) regardless of the position of the range block being encoding, the idea of the dynamic search method is simple, but effective, and it is based on starting the search from the closest domain block to the range block that needs to be encoded. These two search schemes are tested under different matching threshold values, in which the search is terminated whenever a domain block with an acceptable matching level is found. To make the study comprehensive, the test is performed for different image sizes and types, range block and partitioning step sizes, and quantization levels. The experimental results show the significant impact of using the dynamic search method instead of the conventional search method specifically when the threshold is large. For the best encoding parameters, the improvement amount that can be achieved is near to 90 % in terms of search reduction and 1.6 dB PSNR in terms of image quality. INDEX TERMS Fractal image compression, fast search method, full search scheme.

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Adaptive searchless fractal image compression in DCT domain

Cited by 11 publications

References 14 publications

Robust features fusion utilization for supervised palmprint recognition

Robust features fusion utilization for supervised palmprint recognition

Fast fractal image compression algorithm using specific update search

Impact of Spatial Dynamic Search With Matching Threshold Strategy on Fractal Image Compression Algorithm Performance: Study

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