scholarly journals The fidelity of compressed and interpolated medical images

2020 ◽  
pp. 1-11
Author(s):  
Ilona Anna Urbaniak ◽  
Macin Wolter
Keyword(s):  
Medical Images ◽  
Imaging Modality ◽  
Similarity Index ◽  
Objective Measure ◽  
Image Data ◽  
Structural Similarity ◽  
Lossy Compression ◽  
Structural Similarity Index ◽  
Regulatory Bodies

Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory bodies. As expected, increasing the degree of compression leads to decreasing image fidelity. The extent of allowable irreversible compression is dependent on the imaging modality and the nature of the image pathology as well as anatomy. Interpolation, which often causes image distortion, has been extensively used to rescale images during radiological diagnosis. This work attempts to assess the quality of medical images after the application of lossy compression followed by rescaling. This research proposes a fullreference objective measure of quality for medical images that considers their deterministic and statistical properties. Statistical features are acquired from the frequency domain of the signal and are combined with elements of the structural similarity index (SSIM). The aim is to construct a model that is specialized for medical images and that could serve as a predictor of quality.

scholarly journals Assessing Quality of Spatial Models Using the Structural Similarity Index and Posterior Predictive Checks

2014 ◽  
Vol 46 (1) ◽  
pp. 53-74 ◽  
Author(s):  
Colin Robertson ◽  
Jed A. Long ◽  
Farouk S. Nathoo ◽  
Trisalyn A. Nelson ◽  
Cameron C. F. Plouffe
Keyword(s):  
Spatial Models ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Posterior Predictive Checks

scholarly journals Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

2021 ◽  
Vol 12 (1) ◽  
pp. 1-9
Author(s):  
Jelena Vlaović ◽  
Drago Žagar ◽  
Snježana Rimac-Drlje ◽  
Mario Vranješ
Keyword(s):  
Quality Assessment ◽  
Signal To Noise Ratio ◽  
Video Quality ◽  
Similarity Index ◽  
Structural Similarity ◽  
Video Sequences ◽  
Large Set ◽  
Signal To Noise ◽  
Structural Similarity Index

With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.

scholarly journals Fast Continuous Structural Similarity Patch Based Arbitrary Style Transfer

2019 ◽  
Vol 9 (16) ◽  
pp. 3304
Author(s):  
Bing Wu ◽  
Youdong Ding ◽  
Qingshuang Dong
Keyword(s):  
Similarity Index ◽  
Structural Similarity ◽  
Spatial Location ◽  
Natural Images ◽  
Structural Similarity Index ◽  
High Quality ◽  
Style Transfer ◽  
Feed Forward ◽  
Feed Forward Network

Style transfer is using a pair of content and style images to synthesize a stylized image which has both the structure of the content image and the style of style image. Existing optimization-based methods are limited in their performance. Some works using a feed-forward network allow arbitrary style transfer but cannot reflect the style. In this paper, we present a fast continuous structural similarity patch based arbitrary style transfer. Firstly, we introduce the structural similarity index (SSIM) to compute the similarity between all of the content and style patches for obtaining their similarity. Then a local style patch choosing procedure is applied to maximize the utilization of all style patches and make the swapped style patch continuous matching with respect to the spatial location of style at the same time. Finally, we apply an efficient trained feed-forward inverse network to obtain the final stylized image. We use more than 80,000 natural images and 120,000 style images to train that feed-forward inverse network. The results show that our method is able to transfer arbitrary style with consistency, and the result comparison stage is made to show the effectiveness and high-quality of our stylized images.

scholarly journals A robust watermarking algorithm for medical images

2020 ◽  
Vol 20 (3) ◽  
pp. 1601
Author(s):  
Ahmed Nagm ◽  
Mohammed Safy
Keyword(s):  
Medical Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Patient Privacy ◽  
Secret Code ◽  
The Mean ◽  
Image Quality Index

<p>Integrated healthcare systems require the transmission of medical images between medical centres. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be considered while watermarking an image. Among these issues, the watermark should be robust against attacks and does not affect the quality of the image. In this paper, a watermarking approach employing a robust dynamic secret code is proposed. This approach is to process every pixel of the digital image and not only the pixels of the regions of non-interest at the same time it preserves the image details. The performance of the proposed approach is evaluated using several performance measures such as the Mean Square Error (MSE), the Mean Absolute Error (MAE), the Peak Signal to Noise Ratio (PSNR), the Universal Image Quality Index (UIQI) and the Structural Similarity Index (SSIM). The proposed approach has been tested and shown robustness in detecting the intentional attacks that change image, specifically the most important diagnostic information.</p>

scholarly journals Symmetric- Based Steganography Technique Using Spiral-Searching Method for HSV Color Images

2019 ◽  
Vol 16 (4) ◽  
pp. 0948
Author(s):  
Raheem Abdul Sahib Ogla
Keyword(s):  
Similarity Index ◽  
Similarity Measures ◽  
Structural Similarity ◽  
Complex Method ◽  
Structural Similarity Index ◽  
Carrier Medium ◽  
Information Measures ◽  
Negative Effect ◽  
Searching Method

Steganography is defined as hiding confidential information in some other chosen media without leaving any clear evidence of changing the media's features. Most traditional hiding methods hide the message directly in the covered media like (text, image, audio, and video). Some hiding techniques leave a negative effect on the cover image, so sometimes the change in the carrier medium can be detected by human and machine. The purpose of suggesting hiding information is to make this change undetectable. The current research focuses on using complex method to prevent the detection of hiding information by human and machine based on spiral search method, the Structural Similarity Index Metrics measures are used to get the accuracy and quality of the retrieved image and to improve its perceived quality. The values of information measures are calculated through practical experiments of (perceptibility, robustness, capacity) by using interpolation technique and structural similarity measures. Experimental results show that the use of these measures (PSNR, MSE, and SSIM) has improved the image quality by 87% and has produced values of PSNR (38-41 dB), MSE = 0.6537 and SSIM= 0.8255. The results also demonstrate a remarkable progress in the field of hiding information and the increasing difficulty of detecting it by humans and machines.

scholarly journals Comparative analysis between non-linear wavelet based image denoising techniques

2018 ◽  
Vol 5 ◽  
pp. 58-67
Author(s):  
Milan Chikanbanjar
Keyword(s):  
Visual Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Hard Thresholding ◽  
Structural Similarity Index ◽  
Thresholding Method ◽  
Non Linear ◽  
Good Quality Image

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.

scholarly journals Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter

2021 ◽  
Vol 36 (1) ◽  
pp. 642-649
Author(s):  
G. Sharvani Reddy ◽  
R. Nanmaran ◽  
Gokul Paramasivam
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Degraded Image ◽  
Restoration Algorithms ◽  
Index Measure

Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).

scholarly journals Number of Pixel Change Rate and Unified Average Changing Intensity for Sensitivity Analysis of Encrypted inSAR Interferogram

2020 ◽  
Vol 25 (5) ◽  
pp. 601-607
Author(s):  
Riad Saidi ◽  
Nada Cherrid ◽  
Tarek Bentahar ◽  
Hicham Mayache ◽  
Atef Bentahar
Keyword(s):  
Satellite Image ◽  
Similarity Index ◽  
Structural Similarity ◽  
Interferometric Imaging ◽  
Structural Similarity Index ◽  
Change Rate ◽  
Gradient Based ◽  
Key Length ◽  
Two Parameters

The transmission of images from satellites to earth is on the brink of many threats which can affect the confidentiality of the data as well as its quality. Several encryption algorithms are used to secure the transmitted images. The objective in this work is to analyze the sensitivity of a particular type of satellite image, which is an interferogram from interferometric imaging systems inSAR system. This image is encrypted by cryptosystem based on the Advanced Encryption Standard with key length of 256 bits (AES-256) standard and the asymmetric Rivest, Shamir & Adelman (RSA) encryption algorithm using Counter-mode encryption (CTR) mode and Output FeedBack (OFB) mode. The analysis made in this paper is carried out on two types of sensitivity. The first analysis is the sensitivity to change of a pixel in the original interferogram and the second is the sensitivity to the key. Two parameters are used to assess sensitivity: The Number of Pixel Change Rate (NPCR) and the Unified Average Changing Intensity (UACI). The obtained results show that the two modes AES-256-OFB and AES-256-CTR are favorable but cannot be implemented on board a satellite without providing a mechanism capable of compensating for the low resistance to error propagation. Metrics on the clear and encrypted interferogram are exploited such as the Structural Similarity Index (SSIM), Gradient-based Structural Similarity (GSSIM), The use of these metrics, allowed us to see that a change of one pixel in the interferogram and the change of the encryption key will affect the quality of the interferogram, as well as a statistical histogram analysis.

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