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Wyszukujesz frazę "structural similarity index" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Visual attention pooling and understanding the structural similarity index in multi-scale analysis
Autorzy:
Bondzulic, B. P.
Petrovic, V. S.
Mitrovic, S. T.
Pavlovic, B. Z.
Andric, M. S.
Powiązania:
https://bibliotekanauki.pl/articles/173580.pdf
Data publikacji:
2014
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
image quality assessment
structural similarity index
visual attention pooling
Opis:
We present a novel spatial pooling strategy and the results of an extensive multi-scale analysis of the well-known structural similarity index metric (SSIM) for objective image quality evaluation. We show, in contrast with some previous studies, that even relatively simple perceptual importance pooling strategies can significantly improve objective metric performance evaluated as the correlation with subjective quality assessment. In particular, we define an attention and quality driven pooling mechanism that focuses structural comparisons within the SSIM model to only those pixels exhibiting significant structural degradations. We show that optimal objective metric performance is achieved over very sparse spatial domains indeed that ignore most of the signal data. We also investigate an explicit breakdown of the structural models within SSIM and show that in combination with the proposed attention and quality driven pooling some of these models represent well performing metrics in their own right, when applied at appropriate scale for which there may not be a single optimal value. Our experiments demonstrate that the augmented SSIM metric using the proposed pooling model provides performance advantage on an extensive LIVE dataset covering hundreds of degraded images and 5 different distortion types compared to both conventional SSIM and state-of-the-art objective quality metrics.
Źródło:
Optica Applicata; 2014, 44, 2; 267-283
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Color image encryption using singular value decomposition in discrete cosine Stockwell transform domain
Autorzy:
Vaish, A.
Kumar, M.
Powiązania:
https://bibliotekanauki.pl/articles/173700.pdf
Data publikacji:
2018
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
discrete cosine Stockwell transform
image encryption
singular value decomposition
mean square error
structural similarity index measurement
Opis:
In this paper, an image encryption technique using singular value decomposition (SVD) and discrete cosine Stockwell transform (DCST) is proposed. The original source image is encrypted using bands of DCST along with the SVD decomposed images. The number of bands in DCST, parameters used to mask the singular values, the way of permutation used to shuffle the values of SVD transformed images and the way of arrangement of SVD matrices are used as encryption keys. It is necessary to have correct knowledge of all the keys along with their respective values, for correct decryption of encrypted images. The robustness and the quality measurement of proposed work are analyzed by comparing it with some existing works.
Źródło:
Optica Applicata; 2018, 48, 1; 25-38
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals
Autorzy:
Upadhyaya, Vivek
Salim, Mohammad
Powiązania:
https://bibliotekanauki.pl/articles/1844532.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
compressive sensing
CS
mean square error
MSE
structural similarity index measure
SSIM
electroencephalogram
EEG
digital signal processing
DSP
block sparse Bayesian learning
BSBL
Opis:
Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 3; 331-336
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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