Content-aware image resizing (or image retargeting) is a technique that resizes images for optimum display on devices with different resolutions and aspect ratios. Traditional objective quality of experience (QoE) assessment methods are not applicable to retargeted images because the size of a retargeted image is different from its source. In this work, three determining factors for humans visual QoE on retargeted images are analyzed. They are global structural dis- tortion (G), local region distortion (L) and loss of salient information (S). Different features are selected to quantify their respective distortion degrees. Then, an objective quality assessment index, called GLS, is proposed to predict viewers' QoE by fusing selected features into one single quality score. Several regression models used for feature fusion are discussed and compared. Experimental results demonstrate that the proposed GLS quality index has stronger correlation with human QoE than other existing objective metrics in retargeted image quality assessment.

In this paper, we present a compressed-domain video retargeting solution that operates without compromising the resizing quality. Existing video retargeting methods operate in the spatial (or pixel) domain. Such a solution is not practical if it is implemented in mobile devices due to its large memory requirement. In the proposed solution, each component of the retargeting system is designed to exploit the low-level compressed domain features extracted from the coded bit stream. For example, motion information is obtained directly from motion vectors. An efficient column shape mesh deformation is employed to solve the difficulty of sophisticated quad-shape mesh deformation in the compressed domain. The proposed solution achieves comparable (or slightly better) visual quality performance as compared with several state-of-the-art pixel-domain retargeting methods at lower computational and memory costs, making content-aware video resizing both scalable and practical in real-world applications.

A new image decomposition scheme, called the adaptive directional total variation (ADTV) model, is proposed to achieve effective segmentation and enhancement for latent fingerprint images in this work. The proposed model is inspired by the classical total variation models, but it differentiates itself by integrating two unique features of fingerprints; namely, scale and orientation. The proposed ADTV model decomposes a latent fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e.g., structured noise) while the texture layer mainly consists of the latent fingerprint. This cartoon-texture decomposition facilitates the process of segmentation, as the region of interest can be easily detected from the texture layer using traditional segmentation methods. The effectiveness of the proposed scheme is validated through experimental results on the entire NIST SD27 latent fingerprint database. The proposed scheme achieves accurate segmentation and enhancement results, leading to improved feature detection and latent matching performance.

In this paper, we analyze the effect of texture regularity on the performance of image resizing (or called image retargeting), and then propose an efficient texture-aware resizing algorithm. Being perceived as unimportant due to spatial homogeneity, textured patterns are largely deformed (or warped) in existing image resizing algorithms. However, arbitrary warping without considering the specific texture property tends to lead to noticeable visual artifacts. To address this issue, we exploit region features, including the scale and the shape information, to preserve both local and global structures. Guided by region and contour information, we propose a mesh-based resizing technique, which is formulated as a nonlinear least squares optimization problem and solved by an iterative Gauss- Newton method. Texture redundancy is effectively reduced through texture regularity analysis and real-time texture synthesis. The superior performance of the proposed image resizing technique is demonstrated by experimental results.

Latent fingerprint detection and segmentation play a critical role in image forensics for law enforcement. Being collected from crime scenes, a latent fingerprint is often mixed with other components such as structured noise or other fingerprints. Existing fingerprint recognition algorithms fail to work properly for latent fingerprint images, since they are mostly applicable under the assumption that the image is already properly segmented and there is no overlap between the target fingerprint and other components. In this work, we present a novel directional total variation (DTV) model to achieve effective latent fingerprint detection and segmentation. As compared with existing total variation models, the proposed DTV model differentiates itself by considering spatial-dependent texture orientations in the TV computation, which is particularly suitable for images with oriented textures. We demonstrate the superior performance of the proposed DTV technique using images from the NIST SD27 latent fingerprint database.