No-Reference Nonuniform Distorted Video Quality Assessment Based on Deep Multiple Instance Learning

Qian, Lihui; Pan, Tianxiang; Zheng, Yunfei; Zhang, Jiajie; Li, Mading; Yu, Bing; Wang, Bin

doi:10.1109/mmul.2020.3034338

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B Quality Models For Omnidirectional Image/video Contents2

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Cited by 4 publications

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“…The overall video quality is then revised by processing viewports with the predicted saliency map. Instead of handling the entire video, Qian et al [38] created a video bag by grouping a specific number of video blocks, which are extracted features to predict the overall quality of the video. However, constructing a 360°video dataset requires a lot of time and resources, and questions such as "how long a video should be to be efficient for evaluating the quality", "in what proper way we should change quality in video adaptively", etc.…”

Section: B Quality Models For Omnidirectional Image/video Contentsmentioning

confidence: 99%