Ja-Ling Wu

Hidden signatures in images

An image authentication technique by embedding each image with a signature so as to discourage unauthorized copying is proposed. The proposed technique could actually survive several kinds of image processing and the JPEG lossy compression.

Range data acquisition using color structured lighting and stereo vision

Abstract This paper presents a new color-lighting/stereo method for 3D range data acquisition by combining color structured lighting and stereo vision. A major advantage of using stereo vision together with color stripes lighting is that there is no need to solve the problem of finding the correspondence between the color stripes projected by the light source and the color stripes observed in the images. That is, the more difficult problem of finding the correct color stripe correspondence problem between the light source and the image is replaced by an easier image-to-image stereo correspondence — which is not only easier than the above lighting-to-image correspondence problem, but also easier than the traditional stereo correspondence because a good color pattern has been projected onto the object. Another advantage of using stereo vision is that there is no need to calibrate the position and orientation for each of the projected light stripes in 3D space. In this work, a pattern of color stripes is projected onto the objects when taking images, after which edge segments are extracted from the acquired stereo image pair, and then used for finding the correct stereo correspondence. A systematic procedure is proposed in this paper for generating good color stripe patterns. To find the correct stereo correspondence, a global search method based on intra-scanline dynamic programming is adopted. A winner-take-all scheme using edge-based inter-scanline consistency is then proposed to refine the results obtained from intra-scanline search. Experimental results have shown that the proposed method can successfully generate a dense range map with only one pair of stereo images.

Quality Assessment of Stereoscopic 3D Image Compression by Binocular Integration Behaviors

The objective approaches of 3D image quality assessment play a key role for the development of compression standards and various 3D multimedia applications. The quality assessment of 3D images faces more new challenges, such as asymmetric stereo compression, depth perception, and virtual view synthesis, than its 2D counterparts. In addition, the widely used 2D image quality metrics (e.g., PSNR and SSIM) cannot be directly applied to deal with these newly introduced challenges. This statement can be verified by the low correlation between the computed objective measures and the subjectively measured mean opinion scores (MOSs), when 3D images are the tested targets. In order to meet these newly introduced challenges, in this paper, besides traditional 2D image metrics, the binocular integration behaviors-the binocular combination and the binocular frequency integration, are utilized as the bases for measuring the quality of stereoscopic 3D images. The effectiveness of the proposed metrics is verified by conducting subjective evaluations on publicly available stereoscopic image databases. Experimental results show that significant consistency could be reached between the measured MOS and the proposed metrics, in which the correlation coefficient between them can go up to 0.88. Furthermore, we found that the proposed metrics can also address the quality assessment of the synthesized color-plus-depth 3D images well. Therefore, it is our belief that the binocular integration behaviors are important factors in the development of objective quality assessment for 3D images.

RoleNet: Movie Analysis from the Perspective of Social Networks

With the idea of social network analysis, we propose a novel way to analyze movie videos from the perspective of social relationships rather than audiovisual features. To appropriately describe roles relationships in movies, we devise a method to quantify relations and construct roles social networks, called RoleNet. Based on RoleNet, we are able to perform semantic analysis that goes beyond conventional feature-based approaches. In this work, social relations between roles are used to be the context information of video scenes, and leading roles and the corresponding communities can be automatically determined. The results of community identification provide new alternatives in media management and browsing. Moreover, by describing video scenes with roles context, social-relation-based story segmentation method is developed to pave a new way for this widely-studied topic. Experimental results show the effectiveness of leading role determination and community identification. We also demonstrate that the social-based story segmentation approach works much better than the conventional tempo-based method. Finally, we give extensive discussions and state that the proposed ideas provide insights into context-based video analysis.

Video Adaptation for Small Display Based on Content Recomposition

The browsing of quality videos on small hand-held devices is a common scenario in pervasive media environments. In this paper, we propose a novel framework for video adaptation based on content recomposition. Our objective is to provide effective small size videos which emphasize the important aspects of a scene while faithfully retaining the background context. That is achieved by explicitly separating the manipulation of different video objects. A generic video attention model is developed to extract user-interest objects, in which a high-level combination strategy is proposed for fusing the adopted three types of visual attention features: intensity, color, and motion. Based on the knowledge of media aesthetics, a set of aesthetic criteria is presented. Accordingly, these objects are well reintegrated with the direct-resized background to optimally match the specific screen sizes. Experimental results demonstrate the efficiency and effectiveness of our approach

A Digital Blind Watermarking for Depth-Image-Based Rendering 3D Images

The content protection for image-based 3D data is getting more importance with the advance of low cost 3D display devices. The depth-image-based rendering (DIBR) 3D image is one of the image-based 3D data which consists of the center image and the depth image generated by the content provider. The left-eye image and the right-eye image are rendered from the center image and the depth image at the content consumer side. The blind watermarking for DIBR 3D image is rarely studied in the literature. In this paper, a novel blind multiple watermarking scheme is proposed to deal with the content protection problem of DIBR 3D images. Besides the usual requirement of mutual orthogonality among reference patterns for multiple watermark embedding, we found that proper embedding order plays an even more important role in watermarking the DIBR 3D images. Experimental results show that the proposed scheme is robust against the JPEG compression and noise adding attacks. More interestingly, it is found that the proposed watermarking can also tolerate large range variations of the depth image during rendering.

On constructing the Huffman-code-based reversible variable-length codes

In this letter, we propose a generic and efficient algorithm that can construct both asymmetrical and symmetrical reversible variable-length codes (RVLCs). Starting from a given Huffman code, the construction is based on two developed codeword selection mechanisms, for the symmetrical case and the asymmetrical case, respectively; it is shown that the two mechanisms possess simple features and can generate efficient RVLCs easily. In addition, two new asymmetrical RVLCs are constructed and shown to be very efficient for further reducing the coding overheads in MPEG-4 when operating in the reversible decoding mode.

Real-time software-based video coder for multimedia communication systems

A novel software-based video compression algorithm, the Popular Video Coder (PVC), is presented in this paper, and a video phone system, the Popular Phone, is also implemented based on the PVC. The PVC simplifies the traditional video coder by removing the transform and the motion estimation parts and modifies the quantizer and entropy coder. Two novel coding algorithms, the adaptive quantizer and the modified windowed Huffman-like coder, are used in the PVC to encode the video data with a quality picture at a high compression ratio. The video quality of the proposed coder is as good as that of the MPEG coder when the input is a low-resolution and slow-motion video, and the computational complexity of the PVC is much lower than that of the Motion Picture Expert Group (MPEG). Since no compression hardware is needed for the PVC to encode and decode video data, the cost and complexity of developing multimedia applications, such as video phone and multimedia e-mail systems, can be greatly reduced. Furthermore, some networking issues, such as error control and flow control, are discussed in connection with applying the PVC to implement the Popular Phone.

Tiling slideshow

This paper presents a new medium, called tiling slideshow, to display photos in a tile-like manner, coordinating with the pace of background music. In contrast to the conventional photo slideshow, multiple photos that have similar characteristics are well arranged and displayed at the same layout. Motivated by the concepts of technical writing, each displaying layout is composed of a larger topic photo and several small-size supportive photos. Based on this idea, the proposed tiling slideshow system consists of three major components: image clustering, music analyzer, and layout organizer. Given the limited displaying space, we consider the context and relationship between photos and model the layout organization as a constrainted optimization problem. Experiments on real consumer photograph collections show that the novel displaying method gives users more pleasant browsing experience than the methods that focus only on single photograph display.

SheepDog: group and tag recommendation for flickr photos by automatic search-based learning

Online photo albums have been prevalent in recent years and have resulted in more and more applications developed to provide convenient functionalities for photo sharing. In this paper, we propose a system named SheepDog to automatically add photos into appropriate groups and recommend suitable tags for users on Flickr. We adopt concept detection to predict relevant concepts of a photo and probe into the issue about training data collection for concept classification. From the perspective of gathering training data by web searching, we introduce two mechanisms and investigate their performances of concept detection. Based on some existing information from Flickr, a ranking-based method is applied not only to obtain reliable training data, but also to provide reasonable group/tag recommendations for input photos. We evaluate this system with a rich set of photos and the results demonstrate the effectiveness of our work.