Content-Based Multi-Operator Retargeting and Its Quality Evaluation

• Main Authors: Dai-Yan Wei, 韋岱延
• Other Authors: Po-Chyi Su
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/w48775

碩士 === 國立中央大學 === 資訊工程學系 === 107 === This research proposes a content-based multi-operator image retargeting scheme, enabling the retargeted images to preserve its content after adaptation in various displays. Besides, a quality evaluation model is also proposed to compare original images and retargeted images. The proposed multi-operator retargeting scheme is termed “SCAN” as it contains Seam caving, Cropping, Adding seams and Normalization (scaling). This research mainly concentrates on improving the step of content-based cropping in SCAN. We classify images into two categories via foreground detection and adopt different types of visual saliency to determine appropriate cropping limits. The face detection is also introduced to protect face areas appearing at the edges of an image from being removed. A building detection mechanism is employed to determine whether a building in an image is significant or not. The experimental shows that the improved multi-operator retargeting scheme can effectively preserve the content and objects’ shape when dealing with various images. In the proposed quality evaluation model, we make use of SIFT Flow to compare the contents of original and retargeted images and identify possible geometric distortion and line distortion. We further consider salient objects and image semantics in the evaluation process. With these attributes, we utilize the neural network regression model to determine the weights of every feature in order to fit the Mean Opinion Score (MOS). The results show that the proposed model is closer to MOS than other evaluation methods.