Predicting Spatio-Temporal Entropic Differences for Robust No Reference Video Quality Assessment

Mitra, Shankhanil; Soundararajan, Rajiv; Channappayya, Sumohana S.

doi:10.1109/lsp.2021.3049682

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Wei et al utilized Semantic Information related two-level network to estimate the image quality [ 53 ]. Entropic differences learned by the CNN network were used to capture distortions in [ 54 ]. In order to enable the model to have the ability of time-series memory, recurrent neural networks are used in many metrics.…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

Jiang

Wang

et al. 2021

Sensors

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Over the past few decades, video quality assessment (VQA) has become a valuable research field. The perception of in-the-wild video quality without reference is mainly challenged by hybrid distortions with dynamic variations and the movement of the content. In order to address this barrier, we propose a no-reference video quality assessment (NR-VQA) method that adds the enhanced awareness of dynamic information to the perception of static objects. Specifically, we use convolutional networks with different dimensions to extract low-level static-dynamic fusion features for video clips and subsequently implement alignment, followed by a temporal memory module consisting of recurrent neural networks branches and fully connected (FC) branches to construct feature associations in a time series. Meanwhile, in order to simulate human visual habits, we built a parametric adaptive network structure to obtain the final score. We further validated the proposed method on four datasets (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) to test the generalization ability. Extensive experiments have demonstrated that the proposed method not only outperforms other NR-VQA methods in terms of overall performance of mixed datasets but also achieves competitive performance in individual datasets compared to the existing state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

Jiang

Wang

et al. 2021

Sensors

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“…It is an urgent task for video quality assessment (VQA) tools to screen these videos according to their quality. However, evaluating the perceptual quality of in-the-wild videos is extremely hard because neither pristine reference nor shooting distortion is available [18].…”

Section: Introductionmentioning

confidence: 99%

StarVQA: Space-Time Attention for Video Quality Assessment

Xing

Wang

et al. 2021

Preprint

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The attention mechanism is blooming in computer vision nowadays. However, its application to video quality assessment (VQA) has not been reported. Evaluating the quality of inthe-wild videos is challenging due to the unknown of pristine reference and shooting distortion. This paper presents a novel spacetime attention network for the VQA problem, named StarVQA. StarVQA builds a Transformer by alternately concatenating the divided space-time attention. To adapt the Transformer architecture for training, StarVQA designs a vectorized regression loss by encoding the mean opinion score (MOS) to the probability vector and embedding a special vectorized label token as the learnable variable. To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer. Various experiments are conducted on the de-facto in-the-wild video datasets, including LIVE-VQC, KoNViD-1k, LSVQ, and LSVQ-1080p. Experimental results demonstrate the superiority of the proposed StarVQA over the state-of-the-art. Code and model will be available at: https://github.com/DVL/StarVQA.

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Texture Information Boosts Video Quality Assessment

Zhang

Wang

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Predicting Spatio-Temporal Entropic Differences for Robust No Reference Video Quality Assessment

Cited by 8 publications

References 32 publications

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

StarVQA: Space-Time Attention for Video Quality Assessment

Texture Information Boosts Video Quality Assessment

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