The field of algorithmically assessing the 3D quality of experience and/or 3D quality is an extremely challenging
one; making it a fertile ground for research. The complexity of the problem, coupled with our yet nascent
understanding of 3D perception and the increasing commercial shift toward 3D entertainment makes the area
of 3D QA interesting, formidable and practically relevant. This article undertakes a brief review of the recent
research in the area of 3D visual quality of experience and quality assessment. We first review literature in the
field of quality of experience which encompasses geometry, visual discomfort etc., and then perform a similar
review in the field of quality assessment which encompasses distortions such as blur, noise, compression etc. We
describe algorithms and databases that have been proposed in the literature for these purposes. We conclude
with a short description of a recent resource - the LIVE 3D IQA database that is the first quality assessment
database which provides researchers with access to true depth information for each of the stereo pairs obtained
from a high-precision range scanner.
A natural scene statistics (NSS) based blind image denoising approach is proposed, where denoising is performed
without knowledge of the noise variance present in the image. We show how such a parameter estimation can
be used to perform blind denoising by combining blind parameter estimation with a state-of-the-art denoising
algorithm.1 Our experiments show that for all noise variances simulated on a varied image content, our approach
is almost always statistically superior to the reference BM3D implementation in terms of perceived visual quality
at the 95% confidence level.
We tracked the points-of-gaze of human observers as they viewed videos drawn from foreign films while engaged
in two different tasks: (1) Quality Assessment and (2) Summarization. Each video was subjected to three possible
distortion severities - no compression (pristine), low compression and high compression - using the H.264
compression standard. We have analyzed these eye-movement locations in detail. We extracted local statistical
features around points-of-gaze and used them to answer the following questions: (1) Are there statistical differences
in variances of points-of-gaze across videos between the two tasks?, (2) Does the variance in eye movements
indicate a change in viewing strategy with change in distortion severity? (3) Are statistics at points-of-gaze different
from those at random locations? (4) How do local low-level statistics vary across tasks? (5) How do
point-of-gaze statistics vary across distortion severities within each task?
Recently, Seshadrinathan and Bovik proposed the Motion-based Video Integrity Evaluation (MOVIE) index
for VQA.1, 2 MOVIE utilized a multi-scale spatio-temporal Gabor filter bank to decompose the videos and to
compute motion vectors. Apart from its psychovisual inspiration, MOVIE is an interesting option for VQA owing
to its performance. However, the use of MOVIE in a practical setting may prove to be difficult owing to the
presence of the multi-scale optical flow computation. In order to bridge the gap between the conceptual elegance
of MOVIE and a practical VQA algorithm, we propose a new VQA algorithm - the spatio-temporal video SSIM
based on the essence of MOVIE. Spatio-temporal video SSIM utilizes motion information computed from a
block-based motion-estimation algorithm and quality measures using a localized set of oriented spatio-temporal
filters. In this paper we explain the algorithm and demonstrate its conceptual similarity to MOVIE; we explore
its computational complexity and evaluate its performance on the popular VQEG dataset. We show that the
proposed algorithm allows for efficient FR VQA without compromising on the performance while retaining the
conceptual elegance of MOVIE.
A crucial step in image compression is the evaluation of its performance, and more precisely the available way
to measure the final quality of the compressed image. Usually, to measure performance, some measure of the
covariation between the subjective ratings and the degree of compression is performed between rated image
quality and algorithm. Nevertheless, local variations are not well taken into account.
We use the recently introduced Maximum Likelihood Difference Scaling (MLDS) method to quantify suprathreshold
perceptual differences between pairs of images and examine how perceived image quality estimated
through MLDS changes the compression rate is increased. This approach circumvents the limitations inherent
to subjective rating methods.
Spatial pooling strategies used in recent Image Quality Assessment (IQA) algorithms have generally been that of
simply averaging the values of the obtained scores across the image. Given that certain regions in an image are
perceptually more important than others, it is not unreasonable to suspect that gains can be achieved by using
an appropriate pooling strategy. In this paper, we explore two hypothesis that explore spatial pooling strategies
for the popular SSIM metrics.1, 2 The first is visual attention and gaze direction - 'where' a human looks. The
second is that humans tend to perceive 'poor' regions in an image with more severity than the 'good' ones - and
hence penalize images with even a small number of 'poor' regions more heavily. The improvements in correlation
between the objective metrics' score and human perception is demonstrated by evaluating the performance of
these pooling strategies on the LIVE database3 of images.