Fabrizio Guerrini

High Dynamic Range Image Watermarking Robust Against Tone-Mapping Operators

High dynamic range (HDR) images represent the future format for digital images since they allow accurate rendering of a wider range of luminance values. However, today special types of preprocessing, collectively known as tone-mapping (TM) operators, are needed to adapt HDR images to currently existing displays. Tone-mapped images, although of reduced dynamic range, have nonetheless high quality and hence retain some commercial value. In this paper, we propose a solution to the problem of HDR image watermarking, e.g., for copyright embedding, that should survive TM. Therefore, the requirements imposed on the watermark encompass imperceptibility, a certain degree of security, and robustness to TM operators. The proposed watermarking system belongs to the blind, detectable category; it is based on the quantization index modulation (QIM) paradigm and employs higher order statistics as a feature. Experimental analysis shows positive results and demonstrates the system effectiveness with current state-of-art TM algorithms.

Generating story variants with constrained video recombination

We present a novel approach to the automatic generation of filmic variants within an implemented Video-Based Storytelling (VBS) system that successfully integrates video segmentation with stochastically controlled re-ordering techniques and narrative generation via AI planning. We have introduced flexibility into the video recombination process by sequencing video shots in a way that maintains local video consistency and this is combined with exploitation of shot polysemy to enable shot reuse in a range of valid semantic contexts. Results of evaluations on output narratives using a shared set of video data show consistency in terms of local video sequences and global causality with no loss of generative power.

Representation of signals by local symmetry decomposition

In this paper we propose a segmentation of finite support sequences based on the even/odd decomposition of a signal. The objective is to find a more compact representation of information. To this aim, the paper starts to generalize the even/odd decomposition by concentrating the energy on either the even or the odd part by optimally placing the centre of symmetry. Local symmetry intervals are thus located. The sequence segmentation is further processed by applying an iterative growth on the candidate segments to remove any overlapping portions. Experimental results show that the set of segments can be more eficiently compressed with respect to the DCT transformation of the entire sequence, which corresponds to the near optimal KLT transform of the data chosen for the experiment.

Image Watermarking Robust Against Non-Linear Value-Metric Scaling Based on Higher Order Statistics

A new QIM-based image watermarking system for still images is proposed. The new system is expressly designed to cope with non-linear value-metric scaling attacks such as histogram stretching and gamma correction. By recognizing that any value-metric scaling attack must not change the global appearance of the image, we argue that the watermark should be inserted into high level visual features. We move a first step into this direction by proposing a system embedding the watermark into the kurtosis of selected image blocks. Though the kurtosis is not strictly invariant against non-linear gain, its value tends to remain constant whenever the image content is not altered significantly. The experiments we carried out confirm the validity of the new system, though some problems still need to be solved to make it suitable for real applications

Changing video arrangement for constructing alternative stories

Currently, automatic generation of filmic variants faces a number of key technical issues and thus it usually resorts to the shooting of multiple versions of alternative scenes. However, recent advancements in video analysis has made this objective feasible, though semantic consistency must be somehow preserved. This demo presents a video-based storytelling (VBS) system that successfully integrates video processing with narrative generation by means of a shared semantic description. The novel filmic variants are constructed through a flexible video recombination process that takes advantage of the polysemy of baseline video segments. The short output video clips shown in this demo prove how the generated narratives are semantically consistent while keeping generative power intact.

CBCD based on color features and landmark MDS-assisted distance estimation

Content-Based Copy Detection (CBCD) of digital videos is an important research field that aims at the identification of modified copies of an original clip, e.g., on the Internet. In this application, the video content is uniquely identified by the content itself, by extracting some compact features that are robust to a certain set of video transformations. Given the huge amount of data present in online video databases, the computational complexity of the feature extraction and comparison is a very important issue. In this paper, a landmark based multi-dimensional scaling technique is proposed to speed up the detection procedure which is based on exhaustive search and the MPEG-7 Dominant Color Descriptor. The method is evaluated under the MPEG Video Signature Core Experiment conditions, and simulation results show impressive time savings at the cost of a slightly reduced detection performance.

Interactive Film Recombination

In this article, we discuss an innovative media entertainment application called Interactive Movietelling. As an offspring of Interactive Storytelling applied to movies, we propose to integrate narrative generation through artificial intelligence (AI) planning with video processing and modeling to construct filmic variants starting from the baseline content. The integration is possible thanks to content description using semantic attributes pertaining to intermediate-level concepts shared between video processing and planning levels. The output is a recombination of segments taken from the input movie performed so as to convey an alternative plot. User tests on the prototype proved how promising Interactive Movietelling might be, even if it was designed at a proof of concept level. Possible improvements that are suggested here lead to many challenging research issues.

Tracking characters in movies within logical story units

In this paper, we propose a methodology to allow movie character recognition and tracking within movie scenes. In detail, we present a combination of a tracking algorithm robust against the problems of the currently available face detection algorithms and a face recognition process. We test how effective the system is in terms of both face tracking effectiveness and precision-recall results obtained for the recognition of the main characters present in an input movie.

Toward a multi-feature approach to Content-Based Copy Detection

Video Content-Based Copy Detection (CBCD) is an emergent research field which is targeted to the identification of modified copies of an original clip in a given dataset, e.g., on the Internet. As opposed to digital watermarking, the content itself is used to uniquely identify the video through the extraction of features that need to be robust against a certain set of predetermined video attacks. This paper advocates the use of multiple features together with detection performance estimation to construct a flexible video signature instead of a fixed, single feature based one. To combine diverse features, a normalized linear combination is also proposed. The system performance boost is evaluated through the MPEG Video Signature Core Experiment dataset and experimental results show how the proposed signature scheme can achieve impressive improvements with respect to the single feature approach.

Copy detection of immersed content in video databases

Content-Based Copy Detection techniques aim at the identification of modified copies of an original clip in a given database or on the Internet. Usually, a digital fingerprint is computed from the media itself by extracting some features which are later used for the copy detection task. In this paper we propose a system for Video Copy Detection which makes use of several low-level features. In particular we investigate the problem of the detection of copies immersed in dummy multimedia contents. By analyzing the temporal evolution of the selected features, the proposed method detects the relevant part of the query useful for the detection of the original content the query has been extracted from. Experiments conducted on the data set provided by MPEG indicate that a combined use of different features can be useful for isolating immersed copies. Moreover we can see that the various features act differently with respect to the type of modification the video query suffered.