Proceedings ArticleDOI
Deep Blind Video Quality Assessment Based on Temporal Human Perception
Sewoong Ahn,Sanghoon Lee +1 more
- pp 619-623
TLDR
A deep learning scheme named Deep Blind Video Quality Assessment (DeepBVQA) is proposed to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before.Abstract:
The high performance video quality assessment (VQA) algorithm is a necessary skill to provide high quality video to viewers. However, since the nonlinear perception function between the distortion level of the video and the subjective quality score is not precisely defined, there are many limitations in accurately predicting the quality of the video. In this paper, we propose a deep learning scheme named Deep Blind Video Quality Assessment (DeepBVQA) to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before. We used CNN to extract the spatial cues of each video in VQA and proposed new hand-crafted features for temporal cues. Performance experiments show that performance is better than other state-of-the-art no-reference (NR) VQA models and the introduction of hand-crafted temporal features is very efficient in VQA.read more
Citations
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Journal ArticleDOI
Study of Subjective and Objective Quality Assessment of Night-Time Videos
TL;DR: A large-scale night-time VQA database, namely Mobile In-capture Night-time Database for Video Quality (MIND-VQ), is constructed, containing 1181 night- time videos, 435 subjects, and over 130000 opinion scores are constructed, and a new V QA model, namely Visibility-based Night- time Video Quality Assessment Network, VINIA is proposed.
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks
TL;DR: In this article , a semi-supervised learning (SSL) framework is proposed to learn the mapping from video feature representations to the quality scores for NR video quality assessment, which is based on the benefits of consistency regularization and pseudo-labeling.
Proceedings ArticleDOI
Transfer Learning Based Wildlife Recognition for Tele-Observation in Field Occlusion Environment
TL;DR: Comprehensive comparative evaluation proves that the proposed loss can effectively improve the generalization ability of CNN model during the identification of wildlife.
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
TL;DR: In this paper , a transformer-based network architecture was proposed to train a new transformer based network architecture, exploiting quality ranking of different distorted sequences rather than minimizing the L2 or L1 distance from the ground-truth quality labels, and the resulting deep VQA methods (for both full reference and no reference scenarios), FR- and NR-RankDVQA, exhibit consistently higher correlation with perceptual quality compared to the state-of-the-art conventional and deep learning methods, with average SROCC values of
Journal ArticleDOI
Quality Assessment of UGC Videos Based on Decomposition and Recomposition
TL;DR: Sissuire et al. as discussed by the authors proposed a motion-enhanced UGC-VQA method based on decomposition and recomposition to cover the dynamic degradations.
References
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