This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.

In this paper, we present a novel algorithm that scrambles and descrambles a H.264/AVC video element stream. In particular, the proposed method selectively exploits the unique characteristics of H.264/AVC video coding standard. Specifically, we manipulate DCT coefficients and CABAC initialization table, to secure the visual content of H.264/AVC video. This new algorithm is light-weight and able to support the level of security (scrambling strength) as well. Extensive experimental results indicate that the proposed algorithm is highly effective and feasible, yet computationally very efficient.

Adapting multimedia content to users' preferences and perceptual characteristics is a key direction for enabling personalized multimedia services. In this paper, we address the problem of tailoring visual content within the MPEG-21 Digital Item Adaptation (DIA) framework to meet users' visual perception characteristics. In particular, we present methods for adapting visual content to accommodate color vision deficiency and low-vision capabilities. In addition, we present methods for adapting visual content according to user preferences for color temperature. Finally, we report on experiments that adapt visual content within the MPEG-21 DIA framework.

We present a novel technique for detecting the presence of a gradual transition in video sequences and automatically identifying its type. Our scheme focuses on analyzing the characteristics of the underlying special edit effects and estimates actual transitions by polynomial data interpolation. In particular, a B-spline interpolation curve fitting technique is used. We make use of "goodness" of fitting to determine the presence of gradual transitions. Our approach is able to recover the original transition behavior of an edit effect even if it is distorted by various post-processing stages. Our gradual transition detectors have been extensively tested on various genres of real video sequences to evaluate the performance of the proposed algorithms.

In this paper, we introduce a new video summarization procedure that produces a dynamic (video) abstract of the original video sequence.Our technique compactly summarizes a video data by preserving its original temporal characteristics (visual activity) and semantically essential information.It relies on an adaptive nonlinear sampling.The local sampling rate is directly proportional to the amount of visual' activity in localized sub-shot units of the video.The resulting video abstract is highly compact.To get very short, yet semantically meaningful summaries, we propose an event-oriented abstraction scheme, in which two semantic events; emotionaldialogue and violentfeatured action, are characterized and abstracted into the video summary before all other events.If the length of the summary permits, other non key events are then added.

We present a new video summarization procedure that produces a dynamic (video) abstract of the original video sequence. Our approach relies on an adaptive nonlinear sampling of the video. The local sampling rate is directly proportional to the amount of visual activity in localized sub-shot units of the video. The resulting video abstract is highly compact. At playtime, linear interpolation is used to provide the viewer with a summary of the video that accurately preserves the relative length and amount of activity in each sub-shot unit.

We present a novel motion-based video indexing scheme for fast content-based browsing and retrieval in a video database. The proposed technique constructs a dictionary of prototype objects to support query by motion. The first step in our approach extracts moving objects by analyzing layered images constructed from the coarse data in a 3-D wavelet decomposition of the video sequence. These images capture motion information only. Moving objects are modeled as collections of interconnected rigid polygonal shapes in the motion sequences that we derive from the wavelet representation. The motion signatures of the object are computed from the rotational and translational motions associated to the elemental polygons that form the objects. These signatures are finally stored as potential query terms.

We present a novel technique to characterize and index violent scenes in general TV drama and movies. Our goal is to identify violent signatures and localize violent events within a movie to support "high-level" video indexing. In particular, we exploit multiple "audio-visual" signatures to create a perceptual relation for conceptually meaningful violent scene identification. Potential applications are automatic blocking of violence in movies watched by children, hiding violence using data hiding or information filtering and genre classification of digital video database.

We present a new approach to video sequence segmentation into individual shots. Unlike previous approaches, our technique segments the video sequence by combining two streams of information extracted from the visual track with audio track segmentation information. The visual streams of information are computed from the coarse data in a 3-D wavelet decomposition of the video track. They consist of (i) information derived from temporal edges detected along the time evolution of the intensity of each pixel in temporally sub-sampled spatially filtered coarse frames, and (ii) information derived from the coarse spatio-temporal evolution of intra-frame edges in the spatially filtered coarse frames. Our approach is particularly matched to progressively transmitted video.

We present a new video shot classification and clustering technique to support content-based indexing, browsing and retrieval in video databases. The proposed method is based on the analysis of both the audio and visual data tracks. The visual stream is analyzed using a 3-D wavelet transform and segmented into shot units which are matched and clustered by visual content. Simultaneously, speaker changes are detected by tracking voiced phonemes in the audio signal. The clues obtained from the video and speech data are combined to classify and group the isolated video shots. This integrated approach also allows effective indexing of the audio-visual objects in multimedia databases.