Comparison of Different Lossy Image Compression Techniques

Prasanna, Y. Lakshmi; Tarakaram, Y.; Mounika, Y.; Subramani, R

doi:10.1109/icses52305.2021.9633800

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…Thus, the elimination of the eigenvalues with very small values as well as the corresponding eigenvectors can retain the principal information of this matrix, and as a result an approximated matrix with reduced rank can be obtained, which is only slightly different from the original one. A similar idea is also widely used in principal component analysis 23 or lossy compression of images 24 . Note that for the elemental inelastic state matrix

{\bf k}^{\prime\prime}_{pv,i}

, if it is approximated based on the above idea, only a small error will be introduced into the perturbation expansion expression of the structural tangent stiffness and the results of the Woodbury formula.…”

Section: Variant Woodbury Formula With Reduced Idof Numbermentioning

confidence: 99%

“…A similar idea is also widely used in principal component analysis 23 or lossy compression of images. 24 Note that for the elemental inelastic state matrix 𝐤 ′′ 𝑝𝑣,𝑖 , if it is approximated based on the above idea, only a small error will be introduced into the perturbation expansion expression of the structural tangent stiffness and the results of the Woodbury formula. As mentioned above, however, the effect of such error can be minimized by slightly increasing the number of iteration steps or introducing an appropriate error correction method.…”

Section: Second-stage Reduction Of the Idof Numbermentioning

confidence: 99%

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A novel Woodbury solution method for nonlinear seismic response analysis of large‐scale structures

Yu,

2023

Earthq Engng Struct Dyn

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The Woodbury formula is an efficient tool in mathematics to calculate low‐rank perturbation problems and has been applied to improve the computational efficiency of nonlinear seismic response analysis (NSRA) of structures with local nonlinearity. Using the Woodbury formula for NSRA can avoid the time‐consuming recalculation and factorization of the large‐dimensional global stiffness matrix of a structure by only solving a small‐dimensional Schur complement system representing local nonlinearity per iteration. Because the dimension of the Schur complement matrix is determined by the inelastic degree of freedom (IDOF) number, which represents the scale of local nonlinear regions, a small IDOF number is helpful for achieving the high‐efficiency advantage of the Woodbury formula. However, when performing NSRA for large‐scale structures, the IDOF number is usually relatively large, which contradicts the efficiency requirement of the Woodbury formula. To solve this problem and extend the advantage of the Woodbury formula to the NSRA of large‐scale structures, this paper first proposes a two‐stage IDOF number reduction method by eliminating the IDOFs that have insignificant effects on the results, and consequently, a variant Woodbury formula is derived. Because only the principal component in the Schur complement matrix is retained, the dimension of this matrix and the cost for factorizing it can be reduced significantly without losing accuracy, thus greatly improving the efficiency of the proposed method. Moreover, to reduce the additional computational time introduced by the IDOF number reduction procedure and to further improve the computational performance of the proposed method, an OpenMP parallel computational strategy is incorporated. Finally, the validity of the proposed method is verified by implementing incremental dynamic analysis for a large‐scale reinforced concrete structure.

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{\bf k}^{\prime\prime}_{pv,i}

Section: Variant Woodbury Formula With Reduced Idof Numbermentioning

confidence: 99%

Section: Second-stage Reduction Of the Idof Numbermentioning

confidence: 99%

A novel Woodbury solution method for nonlinear seismic response analysis of large‐scale structures

Yu,

2023

Earthq Engng Struct Dyn

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“…Image compression is a cost-effective tool that preserves expensive resources, i.e., transmission bandwidth and data storage space. The basic categories of Image compression are lossy or lossless (LS) compression (Prasanna et al, 2021;Qasim et al, 2020). Various methods are used for encoding and decoding images for the digital image compression process.…”

Section: Introductionmentioning

confidence: 99%

Improved Median Edge Detection (iMED) for Lossless Image Compression

et al. 2023

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A wide variety of applications are used in lossless image compression models, especially in medical, space, and aerial imaging domains. Predictive coding improves the performance of lossless image compression, which highly relies on entropy error. Lower entropy error results in better image compression. The main focus of this research is to improve the prediction process by minimizing the entropy error. This paper proposes a novel idea for improved Median Edge Detection (iMED) predictor for lossless image compression. MED predictor is improved using k-means clustering and finding the local context of pixels using 20-Dimensional Difference (DDx20) for input images and updates the cluster weights using learning rates (µi) to minimize the prediction errors of pixels. The performance of the proposed predictor is evaluated on the standard grey-scale test images dataset and KODAK image dataset. Results are obtained and compared based on entropy error, bits per pixel (bpp), and computational running time in seconds(s) with the MED, GAP, FLIF, and LBP predictors. The performance of the proposed iMED predictor improves significantly in terms of the entropy error, bpp, and computational running time in seconds(s) after comparison with different state-of-the-art predictors.

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“…It is common to divide image compression algorithms into two groups -lossy and lossless (Prasanna et al, 2021;Manga et al, 2021;Sayood, 2017). In this paper, we concentrate on lossy compression methods since they are able to provide quite large and variable compression ratio (CR) needed in many practical applications (Lukin et al;Bondzulic et al;Ortega et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Strange Images in Remote Sensing and Their Properties

Krivenko

Bondžulić

et al. 2023

UJRS

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Lossy image compression is used in many applications including remote sensing. Image size and number increase and this often leads to the necessity to apply image compression. In lossy compression, it is assumed that rate-distortion curves are monotonous functions and this assumption is put into basis of compression control. However, it has been shown recently that there are grayscale and color images called “strange” for which the rate-distortion curves are not monotonous. In this paper, we demonstrate that some remote sensing images can be strange as well and this takes place for JPEG and some other compression techniques. Analysis of properties for strange images using Spearman rank order correlation coefficient is carried out and it is shown that there several parameters characterizing image complexity that have a rather high correlation with probability that a given image is strange. For example, image entropy is one of such parameters.

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Comparison of Different Lossy Image Compression Techniques

Cited by 11 publications

References 8 publications

A novel Woodbury solution method for nonlinear seismic response analysis of large‐scale structures

A novel Woodbury solution method for nonlinear seismic response analysis of large‐scale structures

Improved Median Edge Detection (iMED) for Lossless Image Compression

Strange Images in Remote Sensing and Their Properties

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