Improved quality of JPEG compressed image using approximate K-SVD algorithm

Neethu, K J; Jabbar, Sherin

doi:10.1109/iciiecs.2015.7193185

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Recently, sparse representation has shown great capabilities in handling problems such as audio processing, 7 data expression, 8 column selection, 9 emotion recognition, 10 face recognition, 11 , 12 image compression, 13 image denoising, 14 color image representation, 15 image super-resolution, 16 medical imaging, 17 motion segmentation 18 and data segmentation, 19 image classification, 20 and texture analysis. 21 In these models, an input testing image or signal is encoded as a sparse linear combination of dictionary or training sample.…”

Section: Introductionmentioning

confidence: 99%

Statistical classification for E-glass fiber fabric defects based on sparse coding

Jing

Ren

et al. 2019

Journal of Engineered Fibers and Fabrics

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In this research, a statistical classification algorithm based on sparse coding is presented to classify the defects on E-glass fiber fabrics adaptively. First, all images are preprocessed by being convolved with the MR8 filter banks to obtain the filter responses. For the filter response space of each type of image, we will learn a Class-specific dictionary, and all the Class-specific dictionaries are concatenated to form a complete dictionary. Then, the reconstructed contribution rate of each atom of the complete dictionary to the image filter response is counted to obtain two types of histogram features of each image. Finally, the improved sparse representation classification is used to classify test defect images based on the histogram features. The proposed adaptive classification method has achieved an average classification accuracy of 96.67% on the dataset collected onsite. The results validate the superiority of the proposed method to E-glass fiber fabrics.

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Section: Introductionmentioning

confidence: 99%

Statistical classification for E-glass fiber fabric defects based on sparse coding

Jing

Ren

et al. 2019

Journal of Engineered Fibers and Fabrics

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“…Truncated SVD a) original image b) truncated SVDNeethu. K. J[6] proposed Improved Quality of JPEG Compressed Image Using Approximate K-SVD Algorithm. JPEG compressed images contain ringing and blocking artifacts, which can be offensive to the viewer above certain compression ratios.…”

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confidence: 99%

Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition

Dhumal¹,

Deshmukh²

2016

IJCA

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Image compression techniques are the most apprehensive topics in today"s high-tech environment. Singular Value Decomposition (SVD) is one of the image compression technique. SVD is an attractive algebraic transform for digital image processing applications. The SVD method can transform matrix A into product, which allows us to refactor a digital image in three orthogonal matrices. The using of singular values of such refactoring allows us to represent the image with a reduced set of values, which can store the useful features of the given original image, also use less storage space of the memory, and achieve the image compression process. In this paper, discuss how SVD is applied to images, the technique of image compression and maintain the quality of the image using SVD and also the algorithm to compress an image using MATLAB.

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Survey on Various Image Compression Techniques Used in Image Processing to Improve the Quality of Image

Chavan¹,

S.Bhore²,

Kute³

et al. 2022

IJARSCT

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This paper presents study of assorted lossy compression techniques. the 2 techniques are Wavelet Difference Reduction (WDR) based compression and Singular Value Decomposition (SVD) based compression and SVD based compression reduces the psycho visual redundancies present within the image through rank reduction technique. WDR may be a lossy compression technique. It gains compression by taking the discrete wavelet transform of the input image so encodes the transform values using difference compression method. Singular Value Decomposition (SVD) is one in every of the simplest compression techniques. SVD based compression technique gives better visual quality at higher singular values. Various compression parameters like PSNR, MSE and compression ratio are evaluated for the assorted techniques. during this survey, compare how SVD is applied to colour images, the technique of compression and maintain the standard of the image using SVD and also the algorithm to compress a picture using image processing tool MATLAB and compared the WDR SVD lossy compression techniques.

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Improved quality of JPEG compressed image using approximate K-SVD algorithm

Cited by 3 publications

References 12 publications

Statistical classification for E-glass fiber fabric defects based on sparse coding

Statistical classification for E-glass fiber fabric defects based on sparse coding

Survey on Comparative Analysis of Various Image Compression Algorithms with Singular Value Decomposition

Survey on Various Image Compression Techniques Used in Image Processing to Improve the Quality of Image

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