14,364 research outputs found
Towards a general parametric model for perceptual video quality estimation
PostprintDuring the last few years, different parametric models were proposed for video quality estimation. Each model uses different parameters as inputs, such as bit rate, frame rate and percentage of packet loss, and each model was designed and tested by their authors for a particular codec, display resolution and/or application. This paper presents a review of the parametric models published by ten different groups of authors. Each model is briefly described, and the relevant parametric formulas are presented. The performance of each model is evaluated and contrasted to the other models, using a common video clips set, in different coding and transmission scenarios. Based on the results, a new and more general parametric model is presented, which takes into account bit rate, frame rate, display resolution, video content and the percentage of packet loss
Characterizing and Improving Stability in Neural Style Transfer
Recent progress in style transfer on images has focused on improving the
quality of stylized images and speed of methods. However, real-time methods are
highly unstable resulting in visible flickering when applied to videos. In this
work we characterize the instability of these methods by examining the solution
set of the style transfer objective. We show that the trace of the Gram matrix
representing style is inversely related to the stability of the method. Then,
we present a recurrent convolutional network for real-time video style transfer
which incorporates a temporal consistency loss and overcomes the instability of
prior methods. Our networks can be applied at any resolution, do not re- quire
optical flow at test time, and produce high quality, temporally consistent
stylized videos in real-time
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking and prediction tasks. We show that our proposed model greatly
outperforms standard deconvolutional methods and GANs for image generation,
producing clear, photo-realistic images. We also develop a convolutional neural
network model for state estimation and compare the result to an Extended Kalman
Filter to estimate robot trajectories. We validate all models on a real robotic
system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA)
201
A Detail Based Method for Linear Full Reference Image Quality Prediction
In this paper, a novel Full Reference method is proposed for image quality
assessment, using the combination of two separate metrics to measure the
perceptually distinct impact of detail losses and of spurious details. To this
purpose, the gradient of the impaired image is locally decomposed as a
predicted version of the original gradient, plus a gradient residual. It is
assumed that the detail attenuation identifies the detail loss, whereas the
gradient residuals describe the spurious details. It turns out that the
perceptual impact of detail losses is roughly linear with the loss of the
positional Fisher information, while the perceptual impact of the spurious
details is roughly proportional to a logarithmic measure of the signal to
residual ratio. The affine combination of these two metrics forms a new index
strongly correlated with the empirical Differential Mean Opinion Score (DMOS)
for a significant class of image impairments, as verified for three independent
popular databases. The method allowed alignment and merging of DMOS data coming
from these different databases to a common DMOS scale by affine
transformations. Unexpectedly, the DMOS scale setting is possible by the
analysis of a single image affected by additive noise.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted
for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the
copyright has been transferred to the IEE
No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences
Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives
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