48,601 research outputs found
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. ->
Update alignment formatting of Table
Learning Single-Image Depth from Videos using Quality Assessment Networks
Depth estimation from a single image in the wild remains a challenging
problem. One main obstacle is the lack of high-quality training data for images
in the wild. In this paper we propose a method to automatically generate such
data through Structure-from-Motion (SfM) on Internet videos. The core of this
method is a Quality Assessment Network that identifies high-quality
reconstructions obtained from SfM. Using this method, we collect single-view
depth training data from a large number of YouTube videos and construct a new
dataset called YouTube3D. Experiments show that YouTube3D is useful in training
depth estimation networks and advances the state of the art of single-view
depth estimation in the wild
KonVid-150k: a dataset for no-reference video quality assessment of videos in-the-wild.
Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild. Deep learning approaches have had limited success due to the small size and diversity of existing VQA datasets, either artificial or authentically distorted. We introduce a new in-the-wild VQA dataset that is substantially larger and diverse: KonVid-150k. It consists of a coarsely annotated set of 153,841 videos having five quality ratings each, and 1,596 videos with a minimum of 89 ratings each. Additionally, we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level spatially pooled deep-features (MLSP). They are exceptionally well suited for training at scale, compared to deep transfer learning approaches. Our best method, MLSP-VQA-FF, improves the Spearman rank-order correlation coefficient (SRCC) performance metric on the commonly used KoNViD-1k in-the-wild benchmark dataset to 0.82. It surpasses the best existing deep-learning model (0.80 SRCC) and hand-crafted feature-based method (0.78 SRCC). We further investigate how alternative approaches perform under different levels of label noise, and dataset size, showing that MLSP-VQA-FF is the overall best method for videos in-the-wild. Finally, we show that the MLSP-VQA models trained on KonVid-150k sets the new state-of-the-art for cross-test performance on KoNViD-1k and LIVE-Qualcomm with a 0.83 and 0.64 SRCC, respectively. For KoNViD-1k this inter-dataset testing outperforms intra-dataset experiments, showing excellent generalization
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
Neural view synthesis (NVS) is one of the most successful techniques for
synthesizing free viewpoint videos, capable of achieving high fidelity from
only a sparse set of captured images. This success has led to many variants of
the techniques, each evaluated on a set of test views typically using image
quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research
on how NVS methods perform with respect to perceived video quality. We present
the first study on perceptual evaluation of NVS and NeRF variants. For this
study, we collected two datasets of scenes captured in a controlled lab
environment as well as in-the-wild. In contrast to existing datasets, these
scenes come with reference video sequences, allowing us to test for temporal
artifacts and subtle distortions that are easily overlooked when viewing only
static images. We measured the quality of videos synthesized by several NVS
methods in a well-controlled perceptual quality assessment experiment as well
as with many existing state-of-the-art image/video quality metrics. We present
a detailed analysis of the results and recommendations for dataset and metric
selection for NVS evaluation
ReLaX-VQA:Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consumer devices and undergoes multiple rounds of compression or transcoding before reaching the end user. Therefore, traditional quality metrics that require the original content as a reference cannot be used. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the diversity of video content and the assessment of its quality without reference videos. ReLaX-VQA uses fragments of residual frames and optical flow, along with different expressions of spatial features of the sampled frames, to enhance motion and spatial perception. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features (from Residual Networks and Vision Transformers). Extensive testing on four UGC datasets confirms that ReLaX-VQA outperforms existing NR-VQA methods with an average SRCC value of 0.8658 and PLCC value of 0.8872. We will open source the code and trained models to facilitate further research and applications of NR-VQA: https://github.com/xinyiW915/ReLaX-VQA
ReLaX-VQA:Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consumer devices and undergoes multiple rounds of compression or transcoding before reaching the end user. Therefore, traditional quality metrics that require the original content as a reference cannot be used. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the diversity of video content and the assessment of its quality without reference videos. ReLaX-VQA uses fragments of residual frames and optical flow, along with different expressions of spatial features of the sampled frames, to enhance motion and spatial perception. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features (from Residual Networks and Vision Transformers). Extensive testing on four UGC datasets confirms that ReLaX-VQA outperforms existing NR-VQA methods with an average SRCC value of 0.8658 and PLCC value of 0.8872. We will open source the code and trained models to facilitate further research and applications of NR-VQA: https://github.com/xinyiW915/ReLaX-VQA
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