An optimized fast image resizing method based on content-aware

In traditional image resizing theory based on interpolation, the prominent object may cause distortion, and the image resizing method based on content-aware has become a research focus in image processing because the prominent content and structural features of images are considered in this method. In this paper, we present an optimized fast image resizing method based on content-aware. Firstly, an appropriate energy function model is constructed on the basis of image meshes, and multiple energy constraint templates are established. In addition, this paper deducts the image saliency constraints, and then the problem of image resizing is used to reformulate a kind of convex quadratic program task. Secondly, a method based on neural network is presented in solving the problem of convex quadratic program. The corresponding neural network model is constructed; moreover, some sufficient conditions of the neural network stability are given. Compared with the traditional numerical algorithm such as iterative method, the neural network method is essentially parallel and distributed, which can expedite the calculation speed. Finally, the effects of image resizing by the proposed method and traditional image resizing method based on interpolation are compared by adopting MATLAB software. Experiment results show that this method has a higher performance of identifying the prominent object, and the prominent features can be preserved effectively after the image is resized. It also has the advantages of high portability and good real-time performance with low visual distortion.