Image Interpolation Using Non-adaptive Scaling Algorithms for Multimedia Applications—A Survey

Neetha, C. H.; Moses, C. John; Selvathi, D.

doi:10.1007/978-981-15-8221-9_141

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Image scaling has received great attention in the literature of the past decades, during which many methods based on different approaches have been developed. An overview containing pros and cons for some of them can be found in [30,31].…”

Section: Related Workmentioning

confidence: 99%

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Ramella

Themistoclakis

2022

J Math Imaging Vis

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We present a new image scaling method both for downscaling and upscaling, running with any scale factor or desired size. The resized image is achieved by sampling a bivariate polynomial which globally interpolates the data at the new scale. The method’s particularities lay in both the sampling model and the interpolation polynomial we use. Rather than classical uniform grids, we consider an unusual sampling system based on Chebyshev zeros of the first kind. Such optimal distribution of nodes permits to consider near-best interpolation polynomials defined by a filter of de la Vallée-Poussin type. The action ray of this filter provides an additional parameter that can be suitably regulated to improve the approximation. The method has been tested on a significant number of different image datasets. The results are evaluated in qualitative and quantitative terms and compared with other available competitive methods. The perceived quality of the resulting scaled images is such that important details are preserved, and the appearance of artifacts is low. Competitive quality measurement values, good visual quality, limited computational effort, and moderate memory demand make the method suitable for real-world applications.

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Section: Related Workmentioning

confidence: 99%

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Ramella

Themistoclakis

2022

J Math Imaging Vis

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show abstract

Section: Related Workmentioning

confidence: 99%

Image scaling by de la Vallée-Poussin filtered interpolation

Ramella¹,

Themistoclakis²

2021

Preprint

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We present a new image scaling method both for downscaling and upscaling, running with any scale factor or desired size. It is based on the sampling of an approximating bivariate polynomial, which globally interpolates the data and is defined by a filter of de la Vallée Poussin type whose action ray is suitable regulated to improve the approximation. The method has been tested on a significant number of different image datasets. The results are evaluated in qualitative and quantitative terms and compared with other available competitive methods. The perceived quality of the resulting scaled images is such that important details are preserved, and the appearance of artifacts is low. Very high-quality measure values in downscaling and the competitive ones in upscaling evidence the effectiveness of the method. Good visual quality, limited computational effort, and moderate memory demanding make the method suitable for real-world applications.

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“…[8,22,25,28,50,51]) In literature, downscaling and upscaling are often considered separate problems (see e.g. [7,30,50]), and most of the existing methods are specialized in only one direction, sometimes for a limited range of scaling factors (see e.g. [13,46]).…”

Section: Introductionmentioning

confidence: 99%

Lagrange-Chebyshev Interpolation for image resizing

Ramella¹,

Themistoclakis²

2021

Preprint

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Image resizing is a basic tool in image processing and in literature we have many methods, based on different approaches, which are often specialized in only upscaling or downscaling. In this paper, independently of the (reduced or enhanced) size we aim to get, we approach the problem at a continuous scale where the underlying continuous image is globally approximated by the tensor product Lagrange polynomial interpolating at a suitable grid of first kind Chebyshev zeros. This is a well-known approximation tool that is widely used in many applicative fields, due to the optimal behavior of the related Lebesgue constants. Here we show how Lagrange-Chebyshev interpolation can be fruitfully applied also for resizing an arbitrary digital image in both downscaling and upscaling. The performance of the proposed method has been tested in terms of the standard SSIM and PSNR metrics. The results indicate that, in upscaling, it is almost comparable with the classical Bicubic resizing method with slightly better metrics, but in downscaling a much higher performance has been observed in comparison with Bicubic and other recent methods too. Moreover, in downscaling cases with an odd scale factor, we give an estimate of the mean squared error produced by our method and prove it is theoretically null (hence PSNR equals to infinite and SSIM equals to one) in absence of noise or initial artifacts on the input image.

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Image Interpolation Using Non-adaptive Scaling Algorithms for Multimedia Applications—A Survey

Cited by 4 publications

References 24 publications

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Image scaling by de la Vallée-Poussin filtered interpolation

Lagrange-Chebyshev Interpolation for image resizing

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