Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods

Abdillah, Muhamad Aznar; Yudhana, Anton; Fadlil, Abdul

doi:10.30595/juita.v9i1.8559

Cited by 3 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[17]. Large storage space as a requirement required compression to save as small as possible [18]. Using Figure 1, describe the steps involved in the data compression process in general.…”

Section: Run Length Encoding Compression On Virtual Tour Campus To En...mentioning

confidence: 99%

Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance

Bastian,

Mardiana,

Berliani

et al. 2023

join

View full text Add to dashboard Cite

Virtual tour is one of the rapidly growing applications of multimedia technology which is used for various purposes, including the dissemination of information in an interesting way. The education sector is also not spared from using virtual tour media for promotional purposes, and campuses are no exception to this rule. Large virtual tour content causes high access speed, ultimately reducing the level of comfort experienced by users. This study aims to compress panoramic images displayed on a campus virtual tour using a lossless compression method and the Run Length Encoding (RLE) algorithm. First, panoramic images are combined into one, then individual images are compressed. When recreating a virtual campus tour, compressed images are used so that the amount of data transferred is smaller. The load access speed index increases from 7,233 seconds to 3,789 seconds when images are compressed from 64 bits to 8 bits, with a compression percentage of 27%. The findings from this research are that the RLE algorithm has not been able to compress large files effectively even though it is quite successful in increasing the load access of the virtual tour website.

show abstract

“…[17]. Large storage space as a requirement required compression to save as small as possible [18]. Using Figure 1, describe the steps involved in the data compression process in general.…”

Section: Run Length Encoding Compression On Virtual Tour Campus To En...mentioning

confidence: 99%

Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance

Bastian,

Mardiana,

Berliani

et al. 2023

join

View full text Add to dashboard Cite

show abstract

“…The better the results, which are inversely correlated with MSE and PNSR values, the smaller the MSE value and higher the PNSR value respectively [15,16]. MSE and PNSR were also chosen since they are simple to obtain and frequently utilized in research [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Application of Median and Mean Filtering Methods for Optimizing Face Detection in Digital Photo

Sunardi¹,

Yudhana²,

Wijaya³

2023

RIA

View full text Add to dashboard Cite

Locating the facial region is the aim of face detection in digital images. Face detection issues frequently arise because of digital image noise levels. This study uses median and means filtering techniques to reduce noise in digital photographs. A confusion matrix is used to quantify the median and means filtering methods' accuracy, while the parameters Mean Square Error (MSE) and Peak Noise to Signal Ratio (PNSR) are used to assess these approaches' performance. For this experiment, Viola-Jones was chosen as the face detection method since it is one of the face detection methods with the best accuracy and computational power. According to the outcomes of comparing the median and mean filtering techniques using MSE and PNSR on 50 image samples, the median filtering approach produced the lowest average MSE results, with a value of 19.43, and the median filtering procedure yielded a 13.74 value for the highest PNSR score. The fastest average time was obtained from the mean filtering method with a time of 3.18 seconds. As for the accuracy based on the confusion matrix, these two methods get a good accuracy of 90%. These findings indicate that the Median Filtering approach is superior to the Mean Filtering method in terms of error.

show abstract

“…Olajide and Kolawole (2021) compared the performance of the SVD technique with that of QR decomposition and observed that SVD technique is quite effective in tackling least square and linear system problems than the QR technique. 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.…”

mentioning

confidence: 99%

Application of Singular Value Decomposition technique for compressing images

Audu

2022

Gadau J Pure Allied Sci

View full text Add to dashboard Cite

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.

show abstract

Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods

Cited by 3 publications

References 14 publications

Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance

Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance

Application of Median and Mean Filtering Methods for Optimizing Face Detection in Digital Photo

Application of Singular Value Decomposition technique for compressing images

Contact Info

Product

Resources

About