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Blind/no-reference image quality assessment is performed to accurately evaluate the perceptual quality of a distorted image without prior information from a reference image. In this paper, an effective blind image quality assessment approach based on entropy differences in the discrete cosine transform domain for natural images is proposed. Information entropy is an effective measure of the amount of information in an image. We find the discrete cosine transform coefficient distribution of distorted natural images shows a pulse-shape phenomenon, which directly affects the differences of entropy. Then, a Weibull model is used to fit the distributions of natural and distorted images. This is because the Weibull model sufficiently approximates the pulse-shape phenomenon as well as the sharp-peak and heavy-tail phenomena of natural scene statistics rules. Four features that are related to entropy differences and human visual system are extracted from the Weibull model for three scaling images. Image quality is assessed by the support vector regression method based on the extracted features. This blind Weibull statistics algorithm is thoroughly evaluated using three widely used databases: LIVE, TID2008, and CSIQ. The experimental results show that the performance of the proposed blind Weibull statistics method is highly consistent with that of human visual perception and greater than that of the state-of-the-art blind and full-reference image quality assessment methods in most cases.
Xiaohan Yang; Fan Li; Wei Zhang; Lijun He. Blind Image Quality Assessment of Natural Scenes Based on Entropy Differences in the DCT domain. Entropy 2018, 20, 885 .
AMA StyleXiaohan Yang, Fan Li, Wei Zhang, Lijun He. Blind Image Quality Assessment of Natural Scenes Based on Entropy Differences in the DCT domain. Entropy. 2018; 20 (11):885.
Chicago/Turabian StyleXiaohan Yang; Fan Li; Wei Zhang; Lijun He. 2018. "Blind Image Quality Assessment of Natural Scenes Based on Entropy Differences in the DCT domain." Entropy 20, no. 11: 885.
As an emerging type of Internet of Things (IoT), multimedia IoT (MIoT) has been widely used in the domains of healthcare, smart buildings/homes, transportation and surveillance. In the mobile surveillance system for vehicle tracking, multiple mobile camera nodes capture and upload videos to a cloud server to track the target. Due to the random distribution and mobility of camera nodes, wireless networks are chosen for video transmission. However, the tracking precision can be decreased because of degradation of video quality caused by limited wireless transmission resources and transmission errors. In this paper, we propose a joint source and channel rate allocation scheme to optimize the performance of vehicle tracking in cloud servers. The proposed scheme considers the video content features that impact tracking precision for optimal rate allocation. To improve the reliability of data transmission and the real-time video communication, forward error correction is adopted in the application layer. Extensive experiments are conducted on videos from the Object Tracking Benchmark using the H.264/AVC standard and a kernelized correlation filter tracking scheme. The results show that the proposed scheme can allocate rates efficiently and provide high quality tracking service under the total transmission rate constraints.
Yixin Mei; Fan Li; Lijun He; Liejun Wang. Joint Source and Channel Rate Allocation over Noisy Channels in a Vehicle Tracking Multimedia Internet of Things System. Sensors 2018, 18, 2858 .
AMA StyleYixin Mei, Fan Li, Lijun He, Liejun Wang. Joint Source and Channel Rate Allocation over Noisy Channels in a Vehicle Tracking Multimedia Internet of Things System. Sensors. 2018; 18 (9):2858.
Chicago/Turabian StyleYixin Mei; Fan Li; Lijun He; Liejun Wang. 2018. "Joint Source and Channel Rate Allocation over Noisy Channels in a Vehicle Tracking Multimedia Internet of Things System." Sensors 18, no. 9: 2858.
Guang-Yu Zhu; Li-Jun He; Xue-Wei Ju; Wei-Bo Zhang. A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA. European Journal of Operational Research 2018, 265, 813 -828.
AMA StyleGuang-Yu Zhu, Li-Jun He, Xue-Wei Ju, Wei-Bo Zhang. A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA. European Journal of Operational Research. 2018; 265 (3):813-828.
Chicago/Turabian StyleGuang-Yu Zhu; Li-Jun He; Xue-Wei Ju; Wei-Bo Zhang. 2018. "A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA." European Journal of Operational Research 265, no. 3: 813-828.
Object tracking is an important research direction in computer vision and is widely used in video surveillance, security monitoring, video analysis and other fields. Conventional tracking algorithms perform poorly in specific scenes, such as a target with fast motion and occlusion. The candidate samples may lose the true target due to its fast motion. Moreover, the appearance of the target may change with movement. In this paper, we propose an object tracking algorithm based on motion consistency. In the state transition model, candidate samples are obtained by the target state, which is predicted according to the temporal correlation. In the appearance model, we define the position factor to represent the different importance of candidate samples in different positions using the double Gaussian probability model. The candidate sample with highest likelihood is selected as the tracking result by combining the holistic and local responses with the position factor. Moreover, an adaptive template updating scheme is proposed to adapt to the target’s appearance changes, especially those caused by fast motion. The experimental results on a 2013 benchmark dataset demonstrate that the proposed algorithm performs better in scenes with fast motion and partial or full occlusion compared to the state-of-the-art algorithms.
Lijun He; Xiaoya Qiao; Shuai Wen; Fan Li. Robust Object Tracking Based on Motion Consistency. Sensors 2018, 18, 572 .
AMA StyleLijun He, Xiaoya Qiao, Shuai Wen, Fan Li. Robust Object Tracking Based on Motion Consistency. Sensors. 2018; 18 (2):572.
Chicago/Turabian StyleLijun He; Xiaoya Qiao; Shuai Wen; Fan Li. 2018. "Robust Object Tracking Based on Motion Consistency." Sensors 18, no. 2: 572.
Thiourea is a typical nitrification inhibitor that shows a strong inhibitory effect against the biological nitrification process. The 50% inhibitory concentration (IC50) of thiourea on nitrification was determined to be 0.088 mg g VSS−1, and nitrifiers recovered from the thiourea inhibition after it was completely degraded. The thiourea-degrading ability of the sludge system was improved to 3.06 mg gVSS−1 h−1 through cultivation of thiourea-degrading bacteria by stepwise increasing the influent thiourea concentration. The dominant thiourea-degrading bacteria strain that used thiourea as the sole carbon and nitrogen source in the sludge system was identified as Pseudomonas sp. NCIMB. The results of this study will facilitate further research of the biodegradation characteristics of thiourea and similar pollutants.
Yuan Wang; Xibiao Jin; Lijun He; Wei Zhang. Inhibitory effect of thiourea on biological nitrification process and its eliminating method. Water Science and Technology 2017, 75, 2900 -2907.
AMA StyleYuan Wang, Xibiao Jin, Lijun He, Wei Zhang. Inhibitory effect of thiourea on biological nitrification process and its eliminating method. Water Science and Technology. 2017; 75 (12):2900-2907.
Chicago/Turabian StyleYuan Wang; Xibiao Jin; Lijun He; Wei Zhang. 2017. "Inhibitory effect of thiourea on biological nitrification process and its eliminating method." Water Science and Technology 75, no. 12: 2900-2907.