A hybrid SVD-DWT-DCT based method for image compression and quality measurement of the compressed image

Afrose, Sharmin; Jahan, Sabrin; Chowdhury, Anuva

doi:10.1109/iceeict.2015.7307442

Cited by 2 publications

(2 citation statements)

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“…Hybrid compression improvement not only develops hybrid transform that uses a combination of coding transform and wavelet-based method. A study by Afrose et al [46] proposes a joint of 3 transformation methods: Singular Value Decomposition (SVD), Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). Having used these three methods, the hybrid transform is then combined with Quantization factor and entropy encoding.…”

Section: Hybrid Transformationmentioning

confidence: 99%

Survey of Hybrid Image Compression Techniques

Setyaningsih¹,

Harjoko²

2017

IJECE

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A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a high-quality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique produces a relatively high compression ratio, whereas lossless compression brings about high-quality data reconstruction as the data can later be decompressed with the same results as before the compression. Discussions of the knowledge of and issues about the ongoing hybrid compression technique development indicate the possibility of conducting further researches to improve the performance of image compression method.

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Section: Hybrid Transformationmentioning

confidence: 99%

Survey of Hybrid Image Compression Techniques

Setyaningsih¹,

Harjoko²

2017

IJECE

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“…Again, Abdillah et al (2021) conducted some analysis on image compressing through SVD, Haar-wavelet and coiflets techniques and observed that SVD technique gives higher compression ratio compared to the remaining techniques used. Afrose et al (2015) employ an hybridization by combination of different image processing techniques to compress images and Munshi et al (2021) utilized a supervised learning procedure known as the K-means clustering techniques to process and compress images. The main focus of this research paper is geared towards improvement on the SVD and examining the effect of singular values in application of the proposed SVD technique to image processing.…”

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confidence: 99%

Application of Singular Value Decomposition technique for compressing images

Audu

2022

Gadau J Pure Allied Sci

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Image processing is becoming increasingly important as imaging technology has advanced. A storage constraint might occur even when image quality is an influential factor. This means finding a way to reduce the volume of data while still retaining quality, since compactable systems and minimal space are more desirable in the current computing field. An image compression technique that is frequently used is singular value decomposition (SVD). SVD is a challenging and promising way to loosely compress images, given how many people use images now and how many different kinds of media there are. SVD can be employed to compress digital images by approximating the matrices that generate such images, thereby saving memory while quality is affected negligibly. The technique is a great tool for lowering image dimensions. However, SVD on a large dataset might be expensive and time-consuming. The current study focuses on its improvement and implements the proposed technique in a Python environment. We illustrate the concept of SVD, apply its technique to compress an image through the use of an improved SVD process, and further compare it with some existing techniques. The proposed technique was used to test and evaluate the compression of images under various r-terms, and the singular value characteristics were incorporated into image processing. By utilization of the proposed SVD technique, it was possible to compress a large image of dimension 4928 x 3264 pixels into a reduced 342 x 231 pixels with fair quality. The result has led to better image compression in terms of size, processing time, and errors.

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A hybrid SVD-DWT-DCT based method for image compression and quality measurement of the compressed image

Cited by 2 publications

References 5 publications

Survey of Hybrid Image Compression Techniques

Survey of Hybrid Image Compression Techniques

Application of Singular Value Decomposition technique for compressing images

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