Based on auditory peripheral simulation model, a new Sound Quality Objective Evaluation (SQOE) method is presented,which can be used to model and analyze the impacts of head, shoulder and other parts of human body on ...Based on auditory peripheral simulation model, a new Sound Quality Objective Evaluation (SQOE) method is presented,which can be used to model and analyze the impacts of head, shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique, in which the head related transfer function was taken into account tothe outer ear simulation phase.First, a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built, which mimicsthe physiological functions of the human hearing, simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally, performance comparison was made between the proposed SQOEmethod and ArtemiS software, and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical, simple, and with good evaluation quality.展开更多
Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance...Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.展开更多
Objective evaluations of fused images are important in comparing the performance of different image fusion algorithms. This paper describes a structural similarity metric that does not use a reference image for image ...Objective evaluations of fused images are important in comparing the performance of different image fusion algorithms. This paper describes a structural similarity metric that does not use a reference image for image fusion evaluations. The metric is based on the universal image quality index and addresses not only the similarities between the input images and the fused image, but also the similarities among the input images. The evaluation process distinguishes between complementary information and redundant information using similarities among the input images. The metric uses the information classification to estimate how much structural similarity is preserved in the fused image. Tests demonstrate that the metric correlates well with subjective evaluations of the fused images.展开更多
文摘Based on auditory peripheral simulation model, a new Sound Quality Objective Evaluation (SQOE) method is presented,which can be used to model and analyze the impacts of head, shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique, in which the head related transfer function was taken into account tothe outer ear simulation phase.First, a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built, which mimicsthe physiological functions of the human hearing, simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally, performance comparison was made between the proposed SQOEmethod and ArtemiS software, and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical, simple, and with good evaluation quality.
基金supported by the National Natural Science Foundation of China (No.61872189)。
文摘Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.
基金Supported by the National Natural Science Foundation of China (No.60673024)
文摘Objective evaluations of fused images are important in comparing the performance of different image fusion algorithms. This paper describes a structural similarity metric that does not use a reference image for image fusion evaluations. The metric is based on the universal image quality index and addresses not only the similarities between the input images and the fused image, but also the similarities among the input images. The evaluation process distinguishes between complementary information and redundant information using similarities among the input images. The metric uses the information classification to estimate how much structural similarity is preserved in the fused image. Tests demonstrate that the metric correlates well with subjective evaluations of the fused images.
