Academic literature on the topic 'SSIM index method'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'SSIM index method.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "SSIM index method"
1
Hu, Yan Zhu, Yu Hu, Xin Bo Ai, Hui Yang Zhao, and Zhen Meng. "Clustering stability evaluation method based on SSIM." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261987359. http://dx.doi.org/10.1177/1748302619873592.
Full textAbstract:
In the clustering validity analysis, three main methods including intra-class cohesion, inter-class separation, and artificial judgment index can be used to evaluate the clustering results. If the clustering result is efficient, it means that the clustering stability is better. However, when those methods are used, it is essential to provide the sample data or clustering algorithms in advance. This paper proposes a clustering stability evaluation method based on the Elliptic Fourier Descriptor structural similarity index (EFD-SSIM), which can evaluate the clustering stability only when the clustering result is available. Its mechanism is that cluster is mapped into 2D graphics, and the degree of intra-class cohesion is measured based on the structural similarity (SSIM) on the graphics. As shown by the experimental results, EFD-SSIM has a good evaluation effect and it is consistent with the existing effectiveness evaluation indices of the clustering algorithm.
2
Huang, Lian Fen, Xiao Nan Cui, Jian An Lin, and Zhi Yuan Shi. "A New Reduced-Reference Image Quality Assessment Method Based on SSIM." Applied Mechanics and Materials 55-57 (May 2011): 31–36. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.31.
Full textAbstract:
Because human visual perception is highly adapted for extracting structural information from a scene, but the existing SSIM index is a full reference method which needs entire information of reference images. In this paper, we develop a reduced reference SSIM method and evaluate its performance through a set of assessment criteria, as well as comparison to both EPSNR and SSIM methods on a database of images compressed with JPEG and JPEG2000.
3
Yang, Hong Bo, and Xia Hou. "A New Local Self-Similarity Descriptor Based on Structural Similarity Index." Applied Mechanics and Materials 519-520 (February 2014): 615–22. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.615.
Full textAbstract:
The local self-similarity descriptor is a kind of important image or video local feature description method. It is often used for detection, identification and recognition. In this paper we propose a new local self-similarity descriptor based on structural similarity (SSIM) index. It is showed in this paper that the SSIM Index give very different answers to the question of how self-similar local patches really are. For a given image we compute SSIM index distances between representations for all pairs of spatial-patches and store the results in a Self-Similarity Matrix (SSM) defined as the local feature descriptor. This new method is easily extended to the wavelet representation of images. Comparative evaluation of local feature descriptor with previous methods demonstrates improved performance.
4
Sumarna, Halim Bayuaji, Ema Utami, and Anggit Dwi Hartanto. "Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar." Creative Information Technology Journal 7, no. 2 (2021): 75. http://dx.doi.org/10.24076/citec.2020v7i2.248.
Full textAbstract:
Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi
5
Bakurov, Illya, Marco Buzzelli, Mauro Castelli, Raimondo Schettini, and Leonardo Vanneschi. "Parameters optimization of the Structural Similarity Index." London Imaging Meeting 2020, no. 1 (2020): 19–23. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-13.
Full textAbstract:
We exploit evolutionary computation to optimize the handcrafted Structural Similarity method (SSIM) through a datadriven approach. We estimate the best combination of luminance, contrast and structure components, as well as the sliding window size used for processing, with the objective of optimizing the similarity correlation with human-expressed mean opinion score on a standard dataset. We experimentally observe that better results can be obtained by penalizing the overall similarity only for very low levels of luminance similarity. Finally, we report a comparison of SSIM with the optimized parameters against other metrics for full reference quality assessment, showing superior performance on a different dataset.
6
Afandizadeh Zargari, Shahriar, Amirmasoud Memarnejad, and Hamid Mirzahossein. "A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics." Journal of Advanced Transportation 2021 (June 12, 2021): 1–17. http://dx.doi.org/10.1155/2021/9968698.
Full textAbstract:
The origin-destination (OD) matrices express the number and the pattern of trips distributed between OD pairs. OD matrix structural comparison can be used to identify different mobility patterns in the cities. A comparison of two OD matrices could express their difference from both numerical and structural aspects. Limited methods, such as the mean structural similarity (MSSIM) index and geographical window-based structural similarity index (GSSI), have been developed to compare the structural similarity (SSIM) of two matrices. These methods calculate the structural similarities of two OD matrices by grouping the OD pairs into local windows. The obtained results from the MSSIM entirely depend on the dimensions of the chosen windows. Meanwhile, the GSSI method only focuses on the geographical adjacency and correlation of zones while selecting local windows. Accordingly, this paper developed a novel method named Socioeconomy, Land-use, and Population Structural Similarity Index (SLPSSI) in which local windows are selected according to socioeconomic, land-use, and population properties for SSIM comparison of OD matrices. The proposed method was tested on Tehran’s OD matrix extracted from cell phone Geographic Position System (GPS) data. The advantage of this method over two previous ones was observed in determining the new pattern of trips on local windows and more precise detection of SSIM of the weekdays. The SLPSSI approach is up to 10 percent more accurate than the MSSIM method and up to 5.5 percent more accurate than the GSSI method. The proposed method also had a better performance on sparse OD matrices. It is capable of better determining the SSIM of sparse OD matrices by up to 8% compared with the GSSI method. Finally, the sensitivity analysis results indicate that the suggested method is robust and reliable since it is sensitive to applying both constant and random coefficients.
7
Tran, Dang Ninh, Hans-Jürgen Zepernick, and Thi My Chinh Chu. "Viewing Direction Based LSB Data Hiding in 360° Videos." Electronics 10, no. 13 (2021): 1527. http://dx.doi.org/10.3390/electronics10131527.
Full textAbstract:
In this paper, we propose a viewing direction based least significant bit (LSB) data hiding method for 360° videos. The distributions of viewing direction frequency for latitude and longitude are used to control the amount of secret data to be hidden at the latitude, longitude, or both latitude and longitude of 360° videos. Normalized Gaussian mixture models mimicking the viewing behavior of humans are formulated to define data hiding weight functions for latitude, longitude, and both latitude and longitude. On this basis, analytical expressions for the capacity offered by the proposed method to hide secret data in 360° cover videos are derived. Numerical results for the capacity using different numbers of bit planes and popular 360° video resolutions for data hiding are provided. The fidelity of the proposed method is assessed in terms of the peak signal-to-noise ratio (PSNR), weighted-to-spherically uniform PSNR (WS-PSNR), and non-content-based perceptual PSNR (NCP-PSNR). The experimental results illustrate that NCP-PSNR returns the highest fidelity because it gives lower weights to the impact of LSB data hiding on fidelity outside the front regions near the equator. The visual quality of the proposed method as perceived by humans is assessed using the structural similarity (SSIM) index and the non-content-based perceptual SSIM (NCP-SSIM) index. The experimental results show that both SSIM-based metrics are able to account for the spatial perceptual information of different scenes while the PSNR-based fidelity metrics cannot exploit this information. Furthermore, NCP-SSIM reflects much better the impact of the proposed method on visual quality with respect to viewing directions compared to SSIM.
8
Xie, Xiaolu. "Research on the Image Denoising Method Based on Partial Differential Equations." Cybernetics and Information Technologies 16, no. 5 (2016): 109–18. http://dx.doi.org/10.1515/cait-2016-0057.
Full textAbstract:
Abstract In this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.
9
Chikanbanjar, Milan. "Comparative analysis between non-linear wavelet based image denoising techniques." Journal of Science and Engineering 5 (August 31, 2018): 58–67. http://dx.doi.org/10.3126/jsce.v5i0.22373.
Full textAbstract:
Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) and SSIM (Structural Similarity index). Here, non-linear wavelet transform denoising techniques of natural images are studied, analyzed and compared using thresholding techniques such as soft, hard, semi-soft, LevelShrink, SUREShrink, VisuShrink and BayesShrink. On most of the tests, PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods. For instance, from tests PSNR and SSIM values of lena image for VISUShrink Hard, VISUShrink Soft, VISUShrink Semi Soft, LevelShrink Hard, LevelShrink Soft, LevelShrink Semi Soft, SUREShrink, BayesShrink thresholding methods at the variance of 10 are 23.82, 16.51, 23.25, 24.48, 23.25, 20.67, 23.42, 23.14 and 0.28, 0.28, 0.28, 0.29, 0.22, 0.25, 0.16 respectively which shows that the PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods, and so on. Thus, it can be stated that the performance of LevelShrink Hard thresholding method is better on most of tests.
10
Modupe, Alade Oluwaseun, Amusan Elizabeth Adedoyin, and Adedeji Oluyinka Titilayo. "A Comparative Analysis of LSB, MSB and PVD Based Image Steganography." International Journal of Research and Review 8, no. 9 (2021): 373–77. http://dx.doi.org/10.52403/ijrr.20210948.
Full textAbstract:
Steganography is the art and science of hiding information by embedding data into cover media. Numerous techniques are designed to provide the security for the communication of data over the Internet. A good steganographic algorithm is recognized by the performance of the techniques measured with the support of the performance metrics among which are PSNR, MSE, SSIM, robustness and capacity to hide the information in the cover image. In this paper a comparative analysis of Least Significant Bit (LSB), Most Significant Bit (MSB) and Pixel Value Differencing (PVD) image steganography in grayscale and colored images was performed. Three different cover images was used to hide secret message. A comparative performance analysis of LSB, MSB and PVD methods used in image steganography was performed using peak signal to noise ratio (PSNR), Mean square error (MSE) and Structural Similarity index (SSIM) as performance metrics. LSB technique gives higher PSNR and SSIM values than MSB and PVD method with lower MSE than the other two techniques. Future research can be geared towards investigating the embedding capacity, security, and computational complexity of each technique. Keywords: Least Significant Bit (LSB), Most Significant Bit (MSB), Pixel value differencing (PVD), PSNR, SSIM and MSE,
Dissertations / Theses on the topic "SSIM index method"
1
Tichonov, Jevgenij. "SSIM metodo taikymas didelių vaizdų analizei." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130807_115031-16027.
Full textAbstract:
Darbe nagrinėjamas vienas iš vaizdų kokybės vertinimo metodų (metrikų) – SSIM (struktūrinio panašumo) indekso metodas bei šio metodo naudojimas tiriant didelius vaizdus. Darbo eigoje: • nustatyta kai kurių įgyvendintų SSIM indekso algoritmų problematika, vertinant aukštos raiškos vaizdus; • nustatytos gaunamų skaitinių reikšmių priklausomybės nuo tiriamų vaizdų dydžio; • pagrindžiamas vaizdo duomenų mažinimas SSIM indekso algoritmuose; • pasiūlyti tam tikri sprendimai SSIM indekso algoritmo sudarymui, skirto didelės raiškos vaizdų vertinimui; • palyginti SSIM indekso algoritmų veikimo laikai tarp skirtingų algoritmų; • sukurta programinė įranga, kuri yra pritaikyta Windows operacinei sistemai bei gali būti patogiai įdiegta kompiuteryje. Programoje: – patobulintas SSIM indekso įgyvendinimo algoritmas; – atvaizduojamas SSIM skirtumų žemėlapis; – sukurta patogi vartotojui vizualinė aplinka. Realizuota programinė įranga gali būti naudojama edukaciniais tikslais bei užsakomiesiems apdorotų vaizdų kokybės vertinimo tyrimams.<br>The paper analyzes one of image quality assessment methods (metrics) – SSIM (structural similarity) index method, and this method in order to analyze the large images. In work process: • problems of some SSIM index algorithms for high-resolution images have been identified; • dependence of image size and SSIM index values has been found; • some solutions for SSIM index algorithm for high-resolution images have been proposed; • the image data down sampling in SSIM index algorithms has justified; • SSIM index algorithm run times between different algorithms has been compared; • Software which is designed for MS Windows operating system and can be easily installed on the computer has been developed. In this software: – SSIM index algorithm is updated; – program Displays the SSIM index map; – User-friendly visual environment is developed. Implemented software can be used for educational purposes and commercial use for analyzing processed image quality assessment.
Book chapters on the topic "SSIM index method"
1
Khadtare, Mahesh Satish. "GPU Based Image Quality Assessment using Structural Similarity (SSIM) Index." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8853-7.ch013.
Full textAbstract:
This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.
2
Sankisa, Arun, Katerina Pandremmenou, Peshala V. Pahalawatta, Lisimachos P. Kondi, and Aggelos K. Katsaggelos. "SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch028.
Full textAbstract:
The authors present two methods for examining video quality using the Structural Similarity (SSIM) index: Iterative Distortion Estimate (IDE) and Cumulative Distortion using SSIM (CDSSIM). In the first method, three types of slices are iteratively reconstructed frame-by-frame for three different combinations of packet loss and the resulting distortions are combined using their probabilities to give the total expected distortion. In the second method, a cumulative measure of the overall distortion is computed by summing the inter-frame propagation impact to all frames affected by a slice loss. Furthermore, the authors develop a No-Reference (NR) sparse regression framework for predicting the CDSSIM metric to circumvent the real-time computational complexity in streaming video applications. The two methods are evaluated in resource allocation and packet prioritization schemes and experimental results show improved performance and better end-user quality. The accuracy of the predicted CDSSIM values is studied using standard performance measures and a Quartile-Based Prioritization (QBP) scheme.
3
Shahin, Md, Sanjida Akter, Prome Debnath, and A. K. M. Mostafa Zaman. "Disaster Resilient Rescue of Coastal Community on Cyclone Warning." In Natural Hazards - Impacts, Adjustments and Resilience [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94315.
Full textAbstract:
Bangladesh is in the front line of battlefield of disasters due to geographical location and global warming faced over 200 natural disasters in past 40 years and most of the disasters were cyclones. People need to be evacuated and rescued before a cyclone landfall. In current practice, multipurpose cyclone shelter (MPCS) provides short-term safety for the disaster victims in Bangladesh, where people are rescued after disasters which cannot ensure survival of lives. This study aims to develop a method for efficient evacuation and rescue to reduce death tolls in the events of disasters. This study used Wi-Fi scanner and smartphones to detect people. An inbuilt index that includes name, address, mobile number, photo, service set identifier (SSID), and media access control (MAC) of smartphone was developed for 90 registered participants. In this controlled experiment, few new participants turned on hotspot in every five minutes. A new index of people with MAC/SSID was developed in MPCS simulating an emergency. Missing people were detected by comparing inbuilt index and new index, and ordered them self-evacuation. This method captured 100% evacuees. Most importantly, the proposed method will reduce death tools because the people are rescued earlier to a disaster hits a specific area.
Conference papers on the topic "SSIM index method"
1
Rohani, Mohsen, and Alireza Nasiri Avanaki. "A watermarking method based on optimizing SSIM index by using PSO in DCT domain." In 2009 14th International CSI Computer Conference (CSICC 2009) (Postponed from July 2009). IEEE, 2009. http://dx.doi.org/10.1109/csicc.2009.5349616.
Full text
2
Kaur, Jinder, Gurwinder Kaur, and Ashwani Kumar. "An Improved Method to Remove Salt and Pepper Noise in Noisy Images." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.23.
Full textAbstract:
In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).
3
Becht, Charles, Tony Paulin, Don Edwards, Mark Stonehouse, William Santiago Lock, and Charles Becht. "Sustained Stress Indices (SSI) in the B31.3 2010 Edition." In ASME 2014 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/pvp2014-28267.
Full textAbstract:
The 2010 version of B31.3 introduced sustained stress indices (SSI’s) in paragraph 320. Using methods in references [1],[2],[3],[4],[5], and [11], a test procedure was developed to evaluate these SSI’s for standard metallic piping components. The test procedure has been incorporated into draft versions of B31J so that the sustained stress index can be produced at the same time stress intensification or flexibility factor tests are performed for a particular component. This paper describes the sustained stress index and the B31J test procedure used to determine the SSI.