5,436 research outputs found
Blind Video Quality Assessment at the Edge
Owing to the proliferation of user-generated videos on the Internet, blind
video quality assessment (BVQA) at the edge attracts growing attention. The
usage of deep-learning-based methods is restricted by their large model sizes
and high computational complexity. In light of this, a novel lightweight BVQA
method called GreenBVQA is proposed in this work. GreenBVQA features a small
model size, low computational complexity, and high performance. Its processing
pipeline includes: video data cropping, unsupervised representation generation,
supervised feature selection, and mean-opinion-score (MOS) regression and
ensembles. We conduct experimental evaluations on three BVQA datasets and show
that GreenBVQA can offer state-of-the-art performance in PLCC and SROCC metrics
while demanding significantly smaller model sizes and lower computational
complexity. Thus, GreenBVQA is well-suited for edge devices
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A no-reference optical flow-based quality evaluator for stereoscopic videos in curvelet domain
Most of the existing 3D video quality assessment (3D-VQA/SVQA) methods only consider spatial information by directly using an image quality evaluation method. In addition, a few take the motion information of adjacent frames into consideration. In practice, one may assume that a single data-view is unlikely to be sufficient for effectively learning the video quality. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose an effective multi-view feature learning metric for blind stereoscopic video quality assessment (BSVQA), which jointly focuses on spatial information, temporal information and inter-frame spatio-temporal information. In our study, a set of local binary patterns (LBP) statistical features extracted from a computed frame curvelet representation are used as spatial and spatio-temporal description, and the local flow statistical features based on the estimation of optical flow are used to describe the temporal distortion. Subsequently, a support vector regression (SVR) is utilized to map the feature vectors of each single view to subjective quality scores. Finally, the scores of multiple views are pooled into the final score according to their contribution rate. Experimental results demonstrate that the proposed metric significantly outperforms the existing metrics and can achieve higher consistency with subjective quality assessment
Encoding in the Dark Grand Challenge:An Overview
A big part of the video content we consume from video providers consists of
genres featuring low-light aesthetics. Low light sequences have special
characteristics, such as spatio-temporal varying acquisition noise and light
flickering, that make the encoding process challenging. To deal with the
spatio-temporal incoherent noise, higher bitrates are used to achieve high
objective quality. Additionally, the quality assessment metrics and methods
have not been designed, trained or tested for this type of content. This has
inspired us to trigger research in that area and propose a Grand Challenge on
encoding low-light video sequences. In this paper, we present an overview of
the proposed challenge, and test state-of-the-art methods that will be part of
the benchmark methods at the stage of the participants' deliverable assessment.
From this exploration, our results show that VVC already achieves a high
performance compared to simply denoising the video source prior to encoding.
Moreover, the quality of the video streams can be further improved by employing
a post-processing image enhancement method
Quality Assessment of In-the-Wild Videos
Quality assessment of in-the-wild videos is a challenging problem because of
the absence of reference videos and shooting distortions. Knowledge of the
human visual system can help establish methods for objective quality assessment
of in-the-wild videos. In this work, we show two eminent effects of the human
visual system, namely, content-dependency and temporal-memory effects, could be
used for this purpose. We propose an objective no-reference video quality
assessment method by integrating both effects into a deep neural network. For
content-dependency, we extract features from a pre-trained image classification
neural network for its inherent content-aware property. For temporal-memory
effects, long-term dependencies, especially the temporal hysteresis, are
integrated into the network with a gated recurrent unit and a
subjectively-inspired temporal pooling layer. To validate the performance of
our method, experiments are conducted on three publicly available in-the-wild
video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm,
respectively. Experimental results demonstrate that our proposed method
outperforms five state-of-the-art methods by a large margin, specifically,
12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the
second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE,
respectively. Moreover, the ablation study verifies the crucial role of both
the content-aware features and the modeling of temporal-memory effects. The
PyTorch implementation of our method is released at
https://github.com/lidq92/VSFA.Comment: 9 pages, 7 figures, 4 tables. ACM Multimedia 2019 camera ready. ->
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