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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Proceedings ArticleDOI
A Simple Way of Multimodal and Arbitrary Style Transfer
TL;DR: Experimental results show that while being able to transfer an image to multiple domains in various ways, the image quality is highly competitive with contemporary models in style transfer.
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Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment
Wei Zhou,Zhibo Chen +1 more
TL;DR: An efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner and outperforms state-of-the-art quality assessment algorithms.
Proceedings ArticleDOI
Point Cloud Deformation for Single Image 3d Reconstruction
TL;DR: This paper proposes an approach to reconstruct a precise and dense 3d point cloud from a single image that does not require overhead construction, and is efficient and scalable because the number of trainable parameters is independent of the point cloud size.
Journal ArticleDOI
No-reference video quality assessment based on modeling temporal-memory effects
TL;DR: Short-term spatio-temporal feature fusion benefits the modeling of interaction between spatial and temporal cues in VQA tasks, long-term sequence fusion further improves the performance, and a strong correlation with human subjective judgment is achieved.
Proceedings ArticleDOI
No-Reference Video Quality Assessment Based On Similarity Map Estimation
TL;DR: An encoder-decoder model is proposed to predict pixel-by-pixel similarity maps from the distorted video to exploit the temporal perception mechanism of the human visual system (HVS) and outperforms state-of-the-art NR-VQA metrics.
References
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