Yazhen Zhou

Beijing Academy of Artificial Intelligence

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Hongyan Liu

Beijing University of Technology

Nan Guo

Beijing University of Technology

Ke Gu

Beijing Academy of Artificial Intelligence

Shuang Shi

Northwestern Polytechnical University

Yonghui Zhang

Shandong Agricultural University

Guangcheng Wang

Nantong University

Yonghui Zhang

Beijing Municipal Ecology and Environment Bureau

Dakurah Collins Kyefondeme

Beijing University of Technology

Huayi Zhou

Shanghai Jiao Tong University

Ke Gu

Sichuan Agricultural University

Cooperative Institutions

Beijing University of Technology

Beijing Academy of Artificial Intelligence

Beijing Municipal Ecology and Environment Bureau

Ministry of Education of the People's Republic of China

Dalian University of Technology

Shanghai Jiao Tong University

Jilin University

Sichuan Agricultural University

Peking University

Shanxi Medical University

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Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains

2021 China Automation Congress (CAC) (2021)

Guangcheng Wang Nan Guo Hongyan Liu Yonghui Zhang Yazhen Zhou

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, Many DIBR-synthesized image quality assessment (IQA) algorithms based on quantifying the distortion existed in texture images have been proposed. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to evaluate the visual quality of DIBR-synthesized images accurately. To this end, we present a DIBR-synthesized image quality assessment metric with multi-modal information (i.e. integrating texture and depth information), dubbed as MMI. MMI predicts the quality of DIBR-synthesized images by jointly measuring the texture structure and depth structure of the synthesized image. The design principle of our MMI is that the local geometric distortion, introduced by DIBR techniques in the hole-filling process, destroys the texture structure and the depth structure of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality by a joint representation of texture structure and depth structure. Experiments show that our MMI is better than the competing state-of-the-art IQA algorithms in predicting DIBR-synthesized image quality.

View synthesis

Distortion (music)

Texture (cosmology)

10.1109/cac53003.2021.9728706

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Remote Sensing Inversion for River Turbidity Estimation Based on Noise Injection and Ensemble Learning

2021 China Automation Congress (CAC) (2021)

Yazhen Zhou Shuang Shi Hongyan Liu Yonghui Zhang Ke Gu

Turbidity is one main indicator of quality typically used for monitoring water pollution and water eutrophication. The monitoring technology of river water quality can be divided into contact monitoring technology based on physical sensors and non-contact monitoring technology based on remote sensing satellites. The traditional contact monitoring technology has limited monitoring spatial resolution. As a new technology, the remote sensing monitoring technology of the river can conveniently solve the above-mentioned shortcomings and realize the coverage of the whole river basin. However, due to the scarcity of remote sensing data and the measurement errors of remote sensing satellites themselves, learning-based turbidity measurement methods have weak generalization ability and thus are easily prone to produce large deviations. To effectively address such difficulty, a noise injection method is introduced into a river turbidity measurement model based on ensemble learning. To be specific, we randomly generate many noise sets and add them to the remote sensing data to make it closer to the real application scene, and then adopt the ensemble learning model to estimate river turbidity. Experiments validate that the random noise injection strategy can effectively improve the generalization performance of river turbidity measurement.

Turbidity

10.1109/cac53003.2021.9727612

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Adaptive Enhancement Technology for Screen Content Photos

Communications in computer and information science (2021)

Yazhen Zhou Dakurah Collins Kyefondeme Huayi Zhou Min Zhao Nan Guo

Content (measure theory)

10.1007/978-981-16-1194-0_10

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