Proceedings ArticleDOI
Spatio-Temporal Interactive Laws Feature Correlation Method to Video Quality Assessment
Kuan-Hsien Liu,Tsung-Jung Liu,Hsin-Hua Liu,Soo-Chang Pei +3 more
- pp 1-6
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TLDR
The extensive experiments in the LIVE Video Quality Database suggest the proposed video quality assessment model has superior correlation performance with human visual perception than other state-of-the-art methods.Abstract:
In this work, we proposed a full-reference method to estimate video quality. First, we decompose the video into one spatial image and two spatiotemporal slice images. Then for each one of them, sixteen Laws texture filters are applied to generate nine different Laws feature maps. In order to compare the similarity degree of these feature maps obtained from both original and distorted videos, we compute the twodimensional correlation coefficients. Since the correlation coefficients are computed for each frame and spatiotemporal slice, we only choose four statistical values to represent them to reduce the complexity. Lastly, the regression approach is chosen to learn the mapping relationship between the selected features and subjective quality scores. The extensive experiments in the LIVE Video Quality Database suggest our proposed video quality assessment model has superior correlation performance with human visual perception than other state-of-the-art methods.read more
Citations
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No-Reference Video Quality Assessment by A Cascade Combination of Neural Networks and Regression Model
TL;DR: A general-purpose no-reference video quality assessment (VQA) metric based on the cascade combination of 2D convolutional neural network, multi-layer perceptron, and support vector regression model, which demonstrates that this method is competitive with other full-reference and NR VQA metrics.
References
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Eric C. Larson,Damon M. Chandler +1 more
TL;DR: A quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies, local luminance and contrast masking and changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images.