首页|Chongqing University Researcher Illuminates Research in Pattern Recognition and Artificial Intelligence (Saliency and Depth-Aware Full Reference 360-Degree Image Quality Assessment)
Chongqing University Researcher Illuminates Research in Pattern Recognition and Artificial Intelligence (Saliency and Depth-Aware Full Reference 360-Degree Image Quality Assessment)
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World Scientific
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on pattern recognition and artificial intelligence. According to news originating from Chongqing, People’s Republic of China, by NewsRx correspondents, research stated, “With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably.” Financial supporters for this research include Nsfc; Key Projects of Basic Strengthening Plan; Chongqing Talent; Joint Equipment Pre Research And Key Fund Project of The Ministry of Education; Natural Science Foundation of Chongqing, China; Human Resources And Social Security Bureau Project of Chongqing; Guangdong Oppo Mobile Telecommunications Corporation Ltd.. Our news editors obtained a quote from the research from Chongqing University: “However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full- reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores.”
Chongqing UniversityChongqingPeople’s Republic of ChinaAsiaMachine LearningPattern Recognition and Artificial Intelligence
NETL
NSTL
World Scientific
