1,759 research outputs found
An Efficient Human Visual System Based Quality Metric for 3D Video
Stereoscopic video technologies have been introduced to the consumer market
in the past few years. A key factor in designing a 3D system is to understand
how different visual cues and distortions affect the perceptual quality of
stereoscopic video. The ultimate way to assess 3D video quality is through
subjective tests. However, subjective evaluation is time consuming, expensive,
and in some cases not possible. The other solution is developing objective
quality metrics, which attempt to model the Human Visual System (HVS) in order
to assess perceptual quality. Although several 2D quality metrics have been
proposed for still images and videos, in the case of 3D efforts are only at the
initial stages. In this paper, we propose a new full-reference quality metric
for 3D content. Our method mimics HVS by fusing information of both the left
and right views to construct the cyclopean view, as well as taking to account
the sensitivity of HVS to contrast and the disparity of the views. In addition,
a temporal pooling strategy is utilized to address the effect of temporal
variations of the quality in the video. Performance evaluations showed that our
3D quality metric quantifies quality degradation caused by several
representative types of distortions very accurately, with Pearson correlation
coefficient of 90.8 %, a competitive performance compared to the
state-of-the-art 3D quality metrics
Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion
The use of 3D and stereo imaging is rapidly increasing. Compression,
transmission, and processing could degrade the quality of stereo images.
Quality assessment of such images is different than their 2D counterparts.
Metrics that represent 3D perception by human visual system (HVS) are expected
to assess stereoscopic quality more accurately. In this paper, inspired by
brain sensory/motor fusion process, two stereo images are fused together. Then
from every fused image two synthesized images are extracted. Effects of
different distortions on statistical distributions of the synthesized images
are shown. Based on the observed statistical changes, features are extracted
from these synthesized images. These features can reveal type and severity of
distortions. Then, a stacked neural network model is proposed, which learns the
extracted features and accurately evaluates the quality of stereo images. This
model is tested on 3D images of popular databases. Experimental results show
the superiority of this method over state of the art stereo image quality
assessment approachesComment: 11 pages, 13 figures, 3 table
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics
We present a no reference (NR) quality assessment algorithm for assessing the
perceptual quality of natural stereoscopic 3D (S3D) videos. This work is
inspired by our finding that the joint statistics of the subband coefficients
of motion (optical flow or motion vector magnitude) and depth (disparity map)
of natural S3D videos possess a unique signature. Specifically, we empirically
show that the joint statistics of the motion and depth subband coefficients of
S3D video frames can be modeled accurately using a Bivariate Generalized
Gaussian Distribution (BGGD). We then demonstrate that the parameters of the
BGGD model possess the ability to discern quality variations in S3D videos.
Therefore, the BGGD model parameters are employed as motion and depth quality
features. In addition to these features, we rely on a frame level spatial
quality feature that is computed using a robust off the shelf NR image quality
assessment (IQA) algorithm. These frame level motion, depth and spatial
features are consolidated and used with the corresponding S3D video's
difference mean opinion score (DMOS) labels for supervised learning using
support vector regression (SVR). The overall quality of an S3D video is
computed by averaging the frame level quality predictions of the constituent
video frames. The proposed algorithm, dubbed Video QUality Evaluation using
MOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state of the
art methods when evaluated over the IRCCYN and LFOVIA S3D subjective quality
assessment databases.Comment: 13 PAGES, 7 FIGURES, 7 TABLE
A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System
The problem of stereoscopic image quality assessment, which finds
applications in 3D visual content delivery such as 3DTV, is investigated in
this work. Specifically, we propose a new ParaBoost (parallel-boosting)
stereoscopic image quality assessment (PBSIQA) system. The system consists of
two stages. In the first stage, various distortions are classified into a few
types, and individual quality scorers targeting at a specific distortion type
are developed. These scorers offer complementary performance in face of a
database consisting of heterogeneous distortion types. In the second stage,
scores from multiple quality scorers are fused to achieve the best overall
performance, where the fuser is designed based on the parallel boosting idea
borrowed from machine learning. Extensive experimental results are conducted to
compare the performance of the proposed PBSIQA system with those of existing
stereo image quality assessment (SIQA) metrics. The developed quality metric
can serve as an objective function to optimize the performance of a 3D content
delivery system
MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source
A new stereoscopic image quality assessment database rendered using the
2D-image-plus-depth source, called MCL-3D, is described and the performance
benchmarking of several known 2D and 3D image quality metrics using the MCL-3D
database is presented in this work. Nine image-plus-depth sources are first
selected, and a depth image-based rendering (DIBR) technique is used to render
stereoscopic image pairs. Distortions applied to either the texture image or
the depth image before stereoscopic image rendering include: Gaussian blur,
additive white noise, down-sampling blur, JPEG and JPEG-2000 (JP2K) compression
and transmission error. Furthermore, the distortion caused by imperfect
rendering is also examined. The MCL-3D database contains 693 stereoscopic image
pairs, where one third of them are of resolution 1024x728 and two thirds are of
resolution 1920x1080. The pair-wise comparison was adopted in the subjective
test for user friendliness, and the Mean Opinion Score (MOS) can be computed
accordingly. Finally, we evaluate the performance of several 2D and 3D image
quality metrics applied to MCL-3D. All texture images, depth images, rendered
image pairs in MCL-3D and their MOS values obtained in the subjective test are
available to the public (http://mcl.usc.edu/mcl-3d-database/) for future
research and development
Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting
Visual comfort is a quite important factor in 3D media service. Few research
efforts have been carried out in this area especially in case of 3D content
retargeting which may introduce more complicated visual distortions. In this
paper, we propose a Hybrid Distortion Aggregated Visual Comfort Assessment
(HDA-VCA) scheme for stereoscopic retargeted images (SRI), considering
aggregation of hybrid distortions including structure distortion, information
loss, binocular incongruity and semantic distortion. Specifically, a Local-SSIM
feature is proposed to reflect the local structural distortion of SRI, and
information loss is represented by Dual Natural Scene Statistics (D-NSS)
feature extracted from the binocular summation and difference channels.
Regarding binocular incongruity, visual comfort zone, window violation,
binocular rivalry, and accommodation-vergence conflict of human visual system
(HVS) are evaluated. Finally, the semantic distortion is represented by the
correlation distance of paired feature maps extracted from original
stereoscopic image and its retargeted image by using trained deep neural
network. We validate the effectiveness of HDA-VCA on published Stereoscopic
Image Retargeting Database (SIRD) and two stereoscopic image databases IEEE-SA
and NBU 3D-VCA. The results demonstrate HDA-VCA's superior performance in
handling hybrid distortions compared to state-of-the-art VCA schemes.Comment: 13 pages, 11 figures, 4 table
Subjective Assessment of H.264 Compressed Stereoscopic Video
The tremendous growth in 3D (stereo) imaging and display technologies has led
to stereoscopic content (video and image) becoming increasingly popular.
However, both the subjective and the objective evaluation of stereoscopic video
content has not kept pace with the rapid growth of the content. Further, the
availability of standard stereoscopic video databases is also quite limited. In
this work, we attempt to alleviate these shortcomings. We present a
stereoscopic video database and its subjective evaluation. We have created a
database containing a set of 144 distorted videos. We limit our attention to
H.264 compression artifacts. The distorted videos were generated using 6
uncompressed pristine videos of left and right views originally created by
Goldmann et al. at EPFL [1]. Further, 19 subjects participated in the
subjective assessment task. Based on the subjective study, we have formulated a
relation between the 2D and stereoscopic subjective scores as a function of
compression rate and depth range. We have also evaluated the performance of
popular 2D and 3D image/video quality assessment (I/VQA) algorithms on our
database.Comment: 5 pages, 4 figure
Stereoscopic Cinema
Stereoscopic cinema has seen a surge of activity in recent years, and for the
first time all of the major Hollywood studios released 3-D movies in 2009. This
is happening alongside the adoption of 3-D technology for sports broadcasting,
and the arrival of 3-D TVs for the home. Two previous attempts to introduce 3-D
cinema in the 1950s and the 1980s failed because the contemporary technology
was immature and resulted in viewer discomfort. But current technologies --
such as accurately-adjustable 3-D camera rigs with onboard computers to
automatically inform a camera operator of inappropriate stereoscopic shots,
digital processing for post-shooting rectification of the 3-D imagery, digital
projectors for accurate positioning of the two stereo projections on the cinema
screen, and polarized silver screens to reduce cross-talk between the viewers
left- and right-eyes -- mean that the viewer experience is at a much higher
level of quality than in the past. Even so, creation of stereoscopic cinema is
an open, active research area, and there are many challenges from acquisition
to post-production to automatic adaptation for different-sized display. This
chapter describes the current state-of-the-art in stereoscopic cinema, and
directions of future work.Comment: Published as Ronfard, R\'emi and Taubin, Gabriel. Image and Geometry
Processing for 3-D Cinematography, 5, Springer Berlin Heidelberg, pp.11-51,
2010, Geometry and Computing, 978-3-642-12392-
Survey on Error Concealment Strategies and Subjective Testing of 3D Videos
Over the last decade, different technologies to visualize 3D scenes have been
introduced and improved. These technologies include stereoscopic, multi-view,
integral imaging and holographic types. Despite increasing consumer interest;
poor image quality, crosstalk or side effects of 3D displays and also the lack
of defined broadcast standards has hampered the advancement of 3D displays to
the mass consumer market. Also, in real time transmission of 3DTV sequences
over packet-based networks may results in visual quality degradations due to
packet loss and others. In the conventional 2D videos different extrapolation
and directional interpolation strategies have been used for concealing the
missing blocks but in 3D, it is still an emerging field of research. Few
studies have been carried out to define the assessment methods of stereoscopic
images and videos. But through industrial and commercial perspective,
subjective quality evaluation is the most direct way to evaluate human
perception on 3DTV systems. This paper reviews the state-of-the-art error
concealment strategies and the subjective evaluation of 3D videos and proposes
a low complexity frame loss concealment method for the video decoder.
Subjective testing on prominent datasets videos and comparison with existing
concealment methods show that the proposed method is very much efficient to
conceal errors of stereoscopic videos in terms of computation time, comfort and
distortion
Binocular Rivalry - Psychovisual Challenge in Stereoscopic Video Error Concealment
During Stereoscopic 3D (S3D) video transmission, one or both views can be
affected by bit errors and packet losses caused by adverse channel conditions,
delay or jitter. Typically, the Human Visual System (HVS) is incapable of
aligning and fusing stereoscopic content if one view is affected by artefacts
caused by compression, transmission and rendering with distorted patterns being
perceived as alterations of the original which presents a shimmering effect
known as binocular rivalry and is detrimental to a user's Quality of Experience
(QoE). This study attempts to quantify the effects of binocular rivalry for
stereoscopic videos. Existing approaches, in which one or more frames are lost
in one or both views undergo error concealment, are implemented. Then,
subjective testing is carried out on the error concealed 3D video sequences.
The evaluations provided by these subjects were then combined and analysed
using a standard Student t-test thus quantifying the impact of binocular
rivalry and allowing the impact to be compared with that of monocular viewing.
The main focus is implementing error-resilient video communication, avoiding
the detrimental effects of binocular rivalry and improving the overall QoE of
viewers.Comment: 11 pages, 9 Figure
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