A Novel Approach for Computing Quality Map of Visual Information Fidelity Index

2013 ◽  
pp. 163-173
Author(s):  
Yu Shao ◽  
Fuchun Sun ◽  
Hongbo Li ◽  
Ying Liu
Keyword(s):  
Visual Information ◽  
Novel Approach ◽  
Information Fidelity ◽  
Quality Map

scholarly journals Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fayadh Alenezi ◽  
K. C. Santosh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Network Stability ◽  
Signal To Noise ◽  
Convergence Problem ◽  
Information Fidelity ◽  
Medical Image Enhancement

One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

scholarly journals Practical Evaluation of VMAF Perceptual Video Quality for WebRTC Applications

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 854 ◽  
Author(s):  
Boni García ◽  
Luis López-Fernández ◽  
Francisco Gortázar ◽  
Micael Gallego
Keyword(s):  
Visual Information ◽  
Signal To Noise Ratio ◽  
Subjective Evaluation ◽  
Video Quality ◽  
Structural Similarity ◽  
Assessment Model ◽  
Related Services ◽  
Emergent Technologies ◽  
Information Fidelity

WebRTC is the umbrella term for several emergent technologies aimed to exchange real-time media in the Web. Like other media-related services, the perceived quality of WebRTC communication can be measured using Quality of Experience (QoE) indicators. QoE assessment methods can be classified as subjective (users’ evaluation scores) or objective (models computed as a function of different parameters). In this paper, we focus on VMAF (Video Multi-method Assessment Fusion), which is an emergent full-reference objective video quality assessment model developed by Netflix. VMAF is typically used to assess video streaming services. This paper evaluates the use of VMAF in a different type of application: WebRTC. To that aim, we present a practical use case built on the top of well-known open source technologies, such as JUnit, Selenium, Docker, and FFmpeg. In addition to VMAF, we also calculate other objective QoE video metrics such as Visual Information Fidelity in the pixel domain (VIFp), Structural Similarity (SSIM), or Peak Signal-to-Noise Ratio (PSNR) applied to a WebRTC communication in different network conditions in terms of packet loss. Finally, we compare these objective results with a subjective evaluation using a Mean Opinion Score (MOS) scale to the same WebRTC streams. As a result, we found a strong correlation of the subjective video quality perceived in WebRTC video calls with the objective results computed with VMAF and VIFp in comparison with SSIM and PSNR and their variants.

Robustness Analysis of a Hyperchaos-Based Digital Image Encryption Algorithm under Noise Attacks

2019 ◽  
Vol 43 ◽  
pp. 71-83
Author(s):  
Edwin A. Umoh ◽  
Ogechukwu N. Iloanusi
Keyword(s):  
Image Encryption ◽  
Digital Image ◽  
Visual Information ◽  
Signal To Noise Ratio ◽  
Robustness Analysis ◽  
Encryption Algorithm ◽  
Structural Content ◽  
Digital Image Encryption ◽  
Information Fidelity ◽  
Image Encryption Algorithm

Images are susceptible to degradation by noise from different sources as they undergo various processes. The effect of noise degradation affects the visual information fidelity, structural content and the decryption performance of an image encryption algorithm. In this paper, the effects of noise attacks on the performance of a hyperchaos-based digital image encryption algorithm is evaluated. In hyperchaos-based encryption algorithm, chaotic dynamics are used to encrypt the pixels of images. Two noise models, namely Gaussian noise and salt and pepper noise were added to test images, prior to encryption operations, in order to test for the robustness of the algorithm to noise attacks. The mean square error, peak signal – to – noise ratio, structural content and normalized correlation coefficient of the plain and decrypted images were evaluated. The results obtained indicated that noise has insignificant effect on the decryption performance of the algorithm, as the noise-degraded images and their decrypted counterparts were very identical. Thus, the image encryption algorithm is tolerant of noise and can therefore be used in noisy channels.

scholarly journals A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1191
Author(s):  
Sung In Cho ◽  
Jae Hyeon Park ◽  
Suk-Ju Kang
Keyword(s):  
Visual Information ◽  
Mean Squared Error ◽  
Structural Information ◽  
Similarity Index ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Adversarial Network ◽  
Feature Similarity Index ◽  
Structural Loss ◽  
Information Fidelity

We propose a novel generative adversarial network (GAN)-based image denoising method that utilizes heterogeneous losses. In order to improve the restoration quality of the structural information of the generator, the heterogeneous losses, including the structural loss in addition to the conventional mean squared error (MSE)-based loss, are used to train the generator. To maximize the improvements brought on by the heterogeneous losses, the strength of the structural loss is adaptively adjusted by the discriminator for each input patch. In addition, a depth wise separable convolution-based module that utilizes the dilated convolution and symmetric skip connection is used for the proposed GAN so as to reduce the computational complexity while providing improved denoising quality compared to the convolutional neural network (CNN) denoiser. The experiments showed that the proposed method improved visual information fidelity and feature similarity index values by up to 0.027 and 0.008, respectively, compared to the existing CNN denoiser.

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