scholarly journals A Visual Quality Prediction Map for Michigan, USA: An Approach to Validate Spatial Content

2019 ◽  
Author(s):  
Rüya Yilmaz ◽  
Chung Qing Liu ◽  
Jon Bryan Burley
Keyword(s):  
Visual Quality ◽  
Quality Prediction ◽  
Spatial Content

scholarly journals Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

2019 ◽  
Vol 91 ◽  
pp. 54-65 ◽  
Author(s):  
Sebastian Bosse ◽  
Sören Becker ◽  
Klaus-Robert Müller ◽  
Wojciech Samek ◽  
Thomas Wiegand
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Visual Quality ◽  
Quality Prediction

Multi-layer fusion network for blind stereoscopic 3D visual quality prediction

2021 ◽  
Vol 91 ◽  
pp. 116095
Author(s):  
Wujie Zhou ◽  
Xinyang Lin ◽  
Xi Zhou ◽  
Jingsheng Lei ◽  
Lu Yu ◽  
...  
Keyword(s):  
Visual Quality ◽  
Quality Prediction ◽  
Stereoscopic 3D

scholarly journals Quality Prediction of DWT-Based Compression for Remote Sensing Image Using Multiscale and Multilevel Differences Assessment Metric

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Hongxu Jiang ◽  
Kai Yang ◽  
Tingshan Liu ◽  
Yongfei Zhang
Keyword(s):  
Remote Sensing ◽  
Prediction Model ◽  
Image Quality Assessment ◽  
Quality Criteria ◽  
Visual Quality ◽  
Remote Sensing Image ◽  
Subjective Perception ◽  
Quality Prediction ◽  
Compressed Images ◽  
Multiscale Sampling

Accurate assessment and prediction of visual quality are of fundamental importance to lossy compression of remote sensing image, since it is not only a basic indicator of coding performance, but also an important guide to optimize the coding procedure. In the paper, a novel quality prediction model based on multiscale and multilevel distortion (MSMLD) assessment metric is preferred for DWT-based coding of remote sensing image. Firstly, we propose an image quality assessment metric named MSMLD, which assesses quality by calculating distortions in three levels and multiscale sampling between original images and compressed images. The MSMLD method not only has a better consistency with subjective perception values, but also shows the distortion features and visual quality of compressed image well. Secondly, some significant characteristics in spatial and wavelet domain that link well with quality criteria of MSMLD are chosen with multiple linear regression and used to establish a compression quality prediction model of MSMLD. Finally, the quality prediction model is extended to a wider range of compression ratios from 4 : 1 to 20 : 1 and tested with experiment. The experimental results show that the prediction accuracy of the proposed model is up to 98.33%, and its mean prediction error is less than state-of-the-art methods.

Device-Aware Visual Quality Adaptation for Wireless N-Screen Multicast Systems

2013 ◽  
Vol E96.B (12) ◽  
pp. 3181-3189 ◽  
Author(s):  
Inwoong LEE ◽  
Jincheol PARK ◽  
Seonghyun KIM ◽  
Taegeun OH ◽  
Sanghoon LEE
Keyword(s):  
Visual Quality ◽  
Quality Adaptation

Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks

2020 ◽  
Vol 2020 (4) ◽  
pp. 76-1-76-7
Author(s):  
Swaroop Shankar Prasad ◽  
Ofer Hadar ◽  
Ilia Polian
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Channel Noise ◽  
Detection Accuracy ◽  
Noisy Channel ◽  
Noisy Channels ◽  
Reliable Transmission ◽  
Reliable Detection ◽  
Natural Noise ◽  
Will Force

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.

Identifying Deep Sweetspots: Seismically Constrained Reservoir Quality Prediction Offshore Brunei

2016 ◽  
Author(s):  
Stephan Gelinsky ◽  
Sze-Fong Kho ◽  
Irene Espejo ◽  
Matthias Keym ◽  
Jochen Näth ◽  
...  
Keyword(s):  
Reservoir Quality ◽  
Quality Prediction
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