Color Image Compression using PCA

Mofarreh-Bonab, Mohammad; Mofarreh-Bonab, Mostafa

doi:10.5120/19534-1186

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…However, this method assumes that the size of the original size is always equal. Mohammad et.al in [6] modified the equation to take into account of the number of rows and columns in the matrices. Rafael [5] then proposed the use of compression rate instead of compression ratio to better reflect the degree of compression.…”

Section: B the General Pca Algorithmmentioning

confidence: 99%

Medical image compression using block-based PCA algorithm

Lim

Yap

Manap

2014

2014 International Conference on Computer, Communications, and Control Technology (I4CT)

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PCA algorithm can be employed to aid in image compression. In this paper, two extended-PCA algorithms that manipulate the block information of the image are tested and compared. The first algorithm is termed as block-by-block PCA in which general PCA algorithm are applied on each block of the image. In the second algorithm-the block-to-row PCA, all block information are first concatenated into row before general PCA algorithm is then applied on the transformed matrix. Digital fundus image is used as the input image in this work. Using the newly-derived compression ratios, the result shows that block-to-row PCA outperforms block-by-block PCA in terms of image quality and compression rate. At equal block size and compression ratio, block-to-row PCA can achieve higher PSNR than block-to-block PCA. Blocking effects are discernible on the reconstructed image using block-to-block PCA with block size = 16 and compression ratio as low as 0.25 while no apparent distortion are seen on the reconstructed image using block-torow PCA with block size = 32 and compression ratio as high as 0.90.

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Section: B the General Pca Algorithmmentioning

confidence: 99%

Medical image compression using block-based PCA algorithm

Lim

Yap

Manap

2014

2014 International Conference on Computer, Communications, and Control Technology (I4CT)

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“…The explanation of PCA algorithm has been made extensively in [2] and [3]. Here is the succinct description of the general algorithm applied on an input image:…”

Section: A Pca Algorithmmentioning

confidence: 99%

“…Although there are few studies based on PCA [2][3][4][5], the possibility of isolating ROI was not widely tested. The only ROI investigation on medical image compression using PCA to date was done by segmenting the background with the tissue using a global thresholding scheme [6].…”

Section: Introductionmentioning

confidence: 99%

A GUI system for region-based image compression using Principal Component Analysis

Lim

Yap

Manap

2014

2014 International Conference on Computational Science and Technology (ICCST)

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Medical image compression has received great attention due to its increasing need to reduce the image size without compromising the diagnostically important medical information exhibited on the image. Region-based image compression based on Principal Component Analysis (PCA) has not widely been studied. Hence, in this paper, a GUI system that investigates the efficiency of the region-based compression was proposed. In this system, the user is able to trace the desired ROI on the image and the particular ROI selected will be preserved. From this work, it is found that region-based PCA performs better than PCA algorithm in terms of image quality, yielding comparable compression ratio as the PCA algorithm. The GUI system works as a user-friendly tool to compress the image upon the choice of the user to select the ROI and the compression range. Keywords-medical image compression; region of interest (ROI); compression ratio; image qualityI.

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“…PCA is the most common MSA method, and it aims to analyze the joint behavior and the inner relationship between high-dimensional variables [ 27 ]. It has been widely used in data dimension reduction [ 28 ], pattern recognition [ 29 ], image compression [ 30 ], and other various fields [ 31 ]. In the field of environmental protection, PCA is applied in some areas, such as source apportionment [ 32 , 33 , 34 ], evaluating the performance of AQMN and identifying redundant stations [ 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks

Zhao

Cheng

Wang

et al. 2022

IJERPH

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Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, T2 and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020 ∼ 12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.

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Color Image Compression using PCA

Cited by 9 publications

References 5 publications

Medical image compression using block-based PCA algorithm

Medical image compression using block-based PCA algorithm

A GUI system for region-based image compression using Principal Component Analysis

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks

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