Jiangxi University of Finance and Economics

About: Jiangxi University of Finance and Economics is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Fuzzy logic & China. The organization has 2865 authors who have published 3556 publications receiving 41567 citations.

No Reference Quality Assessment for Screen Content Images With Both Local and Global Feature Representation

Abstract: In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods.

Abnormal event detection in crowded scenes based on deep learning

Abstract: In this paper, we propose to use the deep learning technique for abnormal event detection by extracting spatiotemporal features from video sequences. Human eyes are often attracted to abnormal events in video sequences, thus we firstly extract saliency information (SI) of video frames as the feature representation in the spatial domain. Optical flow (OF) is estimated as an important feature of video sequences in the temporal domain. To extract the accurate motion information, multi-scale histogram optical flow (MHOF) can be obtained through OF. We combine MHOF and SI into the spatiotemporal features of video frames. Finally a deep learning network, PCANet, is adopted to extract high-level features for abnormal event detection. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones on the public video database.

A plaintext-related image encryption algorithm based on chaos

Abstract: A symmetric key image cryptosystem based on the piecewise linear map is presented in this paper. In this cryptosystem, the encryption process and the decryption process are exactly same. They both include the same operations of plaintext-related scrambling once, diffusion twice and matrix rotating of 180 degrees four times. The length of secret key in the system is 64d where d is a positive integer. The proposed system can fight against the chosen/known plaintext attacks due to the using of plaintext-related scrambling. The simulate results and comparison analysis show that the proposed system has many merits such as high encryption/decryption speed, large key space, strong key sensitivity, strong plaintext sensitivity, strong cipher-text sensitivity, good statistical properties of cipher images, and large cipher-text information entropy. So the proposed system can be applied to actual communications.