Research on The Evaluate Quality of Image Fusion Based on Visual Information Fidelity

2013 ◽  
Vol 7 (3) ◽  
pp. 284-291
Author(s):  
LI Jing ◽  
GUO Huiling
Keyword(s):  
Image Fusion ◽  
Visual Information ◽  
Information Fidelity

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.

A new image fusion performance metric based on visual information fidelity

2013 ◽  
Vol 14 (2) ◽  
pp. 127-135 ◽  
Author(s):  
Yu Han ◽  
Yunze Cai ◽  
Yin Cao ◽  
Xiaoming Xu
Keyword(s):  
Image Fusion ◽  
Visual Information ◽  
Performance Metric ◽  
Information Fidelity

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

scholarly journals Fusion of Medical Images in Wavelet Domain: A Discrete Mathematical Model

2018 ◽  
Vol 14 (25) ◽  
pp. 1-11
Author(s):  
Satya Prakash Yadav ◽  
Sachin Yadav
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Image Fusion ◽  
Data Transfer ◽  
Discrete Wavelet ◽  
Related Information ◽  
Medical Domain ◽  
Discrete Mathematical Model ◽  
Treatment Procedures

Introduction: Image compression is a great instance for operations in the medical domain that leads to better understanding and implementations of treatment, especially in radiology. Discrete wavelet transform (dwt) is used for better and faster implementation of this kind of image fusion.Methodology: To access the great feature of mathematical implementations in the medical domain we use wavelet transform with dwt for image fusion and extraction of features through images.Results: The predicted or expected outcome must help better understanding of any kind of image resolutions and try to compress or fuse the images to decrease the size but not the pixel quality of the image.Conclusions: Implementation of the dwt mathematical approach will help researchers or practitioners in the medical domain to attain better implementation of the image fusion and data transmission, which leads to better treatment procedures and also decreases the data transfer rate as the size will be decreased and data loss will also be manageable.Originality: The idea of using images may decrease the size of the image, which may be useful for reducing bandwidth while transmitting the images. But the thing here is to maintain the same quality while transmitting data and also while compressing the images.Limitations: As this is a new implementation, if we have committed any mistakes in image compression of medical-related information, this may lead to treatment faults for the patient. Image quality must not be reduced with this implementation.

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 Polarimetric Dehazing Method Based on Image Fusion and Adaptive Adjustment Algorithm

2021 ◽  
Vol 11 (21) ◽  
pp. 10040
Author(s):  
Yu Lei ◽  
Bing Lei ◽  
Yubo Cai ◽  
Chao Gao ◽  
Fujie Wang
Keyword(s):  
Image Fusion ◽  
Degree Of Polarization ◽  
Fusion Technique ◽  
Low Degree ◽  
Low Pass ◽  
Adaptive Adjustment ◽  
Image Fusion Technique ◽  
Focus Plane ◽  
Polarization Camera

To improve the robustness of current polarimetric dehazing scheme in the condition of low degree of polarization, we report a polarimetric dehazing method based on the image fusion technique and adaptive adjustment algorithm which can operate well in many different conditions. A splitting focus plane linear polarization camera was employed to grab the images of four different polarization directions, and the haze was separated from the hazy images by low-pass filtering roughly. Then the image fusion technique was used to optimize the method of estimating the transmittance map. To improve the quality of the dehazed images, an adaptive adjustment algorithm was introduced to adjust the illumination distribution of the dehazed images. The outdoor experiments have been implemented and the results indicated that the presented method could restore the target information obviously, and both the visual effect and quantitative evaluation have been enhanced.

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