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Immersive media, such as free view point video and 360° video, are expected to be dominant as broadcasting services. The light field (LF) imaging is being considered as a next generation imaging technology offering the possibility to provide new services, including six degree-of-freedom video. The drawback of this technology is in the size of the generated content thus requiring novel compression systems and the design of ad-hoc methodologies for evaluating the perceived quality. In this paper, the relation between depth map quality and overall quality of LF image is studied. Next, a reduced reference quality assessment metric for LF images is presented. To predict the quality of distorted LF images, the measure of distortion in the depth map is exploited. To test and validate the proposed framework, a subjective experiment has been performed, and a LF image quality dataset has been created. The dataset is also used for evaluating the performance of state-of-the-art quality metrics, when applied to LF images. The achieved results evidence that the estimated quality score by the proposed framework has a significant correlation with subjective quality rating. Consequently, reference data can be delivered to the clients thus allowing the local estimation of the perceived quality of service.

Recent years have witnessed a rapid growth of user generated videos thanks to the availability of affordable video recording devices and the extreme popularity of social media platforms. Accordingly, there is a challenge for designing highly efficient video quality assessment models to monitor, control, and optimize this content. In this contribution, a novel no-reference video quality metric for user generated video is presented. It exploits the spatial and temporal information contained in the center patch of video frames and a Support Vector Regressor system for computing the objective score. Experimental results show the effectiveness of the proposed approach. To promote reproducible research and public evaluation, an implementation of RM3VQA has been made available online: https://github.com/pramitmazumdar/RM3VQA.

The objective of this work is to evaluate the feasibility of the integration of visual sensors in the framework of GNSS-based vehicle localization systems. Undoubtedly GNSS plays a key role in vehicle localization. However, the impact of GNSS faults (e.g. satellite and constellation failure), signal degradation (e.g. ionospheric scintillations, multipath) and external threats (e.g. jamming and spoofing) need to be taken into account. To address this issue, we propose a three-level localization framework based on the use of additional visual sensors. Thanks to the visual input, the inaccurate GNSS measurements are refined by tracking georeferenced landmarks to obtain the vehicle absolute position. Moreover, the same sensors can be used to obtain the relative position and motion of the vehicle. Every time a new group of landmarks is detected, it is possible to repeat the absolute positioning process to cancel the drift accumulated by the relative localization procedure. This work represents the first step towards the realization of the proposed localization technique. Initial testing to evaluate its suitability has been carried out and is reported hereafter.

Light field imaging is increasingly spreading and its applications are reaching a larger number of users. The quality of the rendered content as perceived by the viewers is a key factor for the future development of this technology. Many factors can cause a degradation of the Light Field quality, from data acquisition/creation to the final rendering. To increase the perceived quality of the Light Field content, as usually performed for 2D and 3D content, processing techniques can be applied. In this work, an approach for enhancing Light Field videos is presented. The proposed approach is based on the characteristics of the human visual system and relies on the estimation of depth, motion and saliency. Those features guide a selective enhancement that is implemented in the Laguerre-Gauss Transform domain. The performed tests show the effectiveness of the proposed method.

Industrial and Automation Control systems traditionally achieved security thanks to the use of proprietary protocols and isolation from the telecommunication networks. Nowadays, the advent of the Industrial Internet of Things poses new security challenges. In this paper, we first highlight the main security challenges that advocate for new risk assessment and security strategies. To this end, we propose a security framework and advanced tools to properly manage vulnerabilities, and to timely react to the threats. The proposed architecture fills the gap between computer science and control theoretic approaches. The physical layers connected to Industrial Control Systems are prone to disrupt when facing cyber-attacks. Considering the modules of the proposed architecture, we focus on the development of a practical framework to compare information about physical faults and cyber-attacks. This strategy is implemented in the ATENA architecture that has been designed as an innovative solution for the protection of critical assets.

Omni-directional imaging records the visual information from any direction with respect to a given view-point. It is gaining consumers’ popularity due to fast spreading of low-cost devices both for acquisition and rendering. The possibility to render the whole surrounding space represents a further step towards immersivity, thus providing the user with the illusion of physically being in a virtual environment. The understanding of visual attention mechanisms for these images is a relevant topic for processing, coding, and exploiting such data. In this contribution, a saliency model for omni-directional images is presented. It is based on the combination of low-level and semantic features. The first ones account for texture, viewport saliency, hue and saturation, while the second are used to take into account the impact of the presence of human subjects on the saliency. The proposed model has been tested in the “Salient360! Visual attention modeling for 360°View the MathML sourceImages” Grand Challenge. The model, the achieved results, and finding/discussions are here presented.

The Internet of Things paradigm improves the classical information sharing scheme. However, it has increased the need for granting the security of the connected systems. In the industrial field, the problem becomes more complex due to the need of protecting a large attack surface while granting the availability of the system and the real time response to the presence of threats. In this contribution, we deal with the injection of tampered data into the communication channel to affect the physical system. The proposed approach relies on designing a secure control system by coding the output matrices according to a secret pattern. This pattern is created by using the Fibonacci p-sequences, numeric sequence depending on a key. The proposed method is validated on the Hydra testbed, emulating the industrial control network of a water distribution system.

Depth-Image-Based-Rendering (DIBR) techniques are essential for three-dimensional (3D) video applications such as 3D Television (3DTV) and Free-Viewpoint Video. However, this process is based on 3D warping and can induce serious distortions whose impact on the perceived quality is far different from the one experienced in the 2D imaging processes. Since quality evaluation of DIBR-synthesized views is fundamental for the design of perceptually friendly 3D video systems, an appropriate objective quality metric targeting the assessment of DIBR-synthesized views is momentous. Most of the 2D objective quality metrics fail in assessing the visual quality of DIBR-synthesized views because they have not been conceived for addressing the specificities of DIBR-related distortions. In this paper, a new full-reference objective quality metric, 3DSwIM (3D Synthesized view Image Quality Metric), dedicated to artifacts detection in DIBR-synthesized view-points is presented. The proposed scheme relies on a comparison of statistical features of wavelet subbands of two input images: the original image and the DIBR-based synthesized image. A registration step is included before the comparison step so that best matching blocks are always compared to ensure "shifting-resilience". In addition, a skin detection step weights the final quality score in order to penalize distorted blocks containing "skin-pixels" based on the assumption that a human observer is most sensitive to impairments affecting human subjects. Experimental tests show that the proposed method outperforms the conventional 2D and DIBR-dedicated quality metrics under test. HighlightsThis paper presents a new full- reference objective quality metric dedicated to artifacts detection in 3D synthesized views.The proposed metric is based on the comparison of statistical features of wavelet subbands of the original image and the DIBR-based synthesized image."Shifting-resilience" is granted by the use of a registration algorithm.The final quality score is weighted depending on the presence of "skin-pixels" based on the assumption that a human observer is more sensitive to impairments affecting human subjects.Experimental tests show that the proposed method outperforms the 2D conventional and DIBR-synthesized views dedicated quality metrics under test.

The security of Cyber Physical Systems is a key factor since these architectures are being applied in many critical scenarios, such as power or water plants, hospitals, etc. In the state of the art, several approaches have been proposed to deal with the possible attacks that could be inferred to the Cyber Physical Systems. This contribution proposes a method for counter fighting the injection of tampered data in the communication channel. If this attack is not timely detected, it may result in severe disruption of the system or even in its complete damage. The proposed approach is based on coding the physical output of the system through permutation matrices whose scheme varies based on a randomly generated sequence. The strength of this method is in reducing the possibility of a successful injection attack while limiting the computational complexity. The experimental tests prove the effectiveness of the proposed scheme in detecting the performed attack while granting the real time constraints of the Cyber Physical Systems.

Light Field imaging derives from the fundamentals of light field sampling, where the spatial information about a scene can be captured with angular information. That is, the Light Field imaging is based on a camera recording information about the intensity of light from the scene and about the direction of the light rays. The acquired data can be shown to the user in different ways, such as image with digitally extended depth of field, 3D, parallax, and 360 degree display. In this contribution, the impact of different rendering techniques on the Quality of Experience is addressed. The achieved results show that the visualization techniques may have different impact on the perceived quality even when the same content is considered.

Evaluation of perceived quality of light field images, as well as testing new processing tools, or even assessing the effectiveness of objective quality metrics, relies on the availability of test dataset and corresponding quality ratings. This paper presents SMART light field image quality dataset. The dataset consists of source images (raw data without optical corrections), compressed images, and annotated subjective quality scores. Furthermore, analysis of perceptual effects of compression on SMART dataset is presented. Next, the impact of image content on the perceived quality is studied with the help of image quality attributes. Finally, the performances of 2-D image quality metrics when applied to light field images are analyzed.

In this work, the problem of dynamic video scene creation obtained by combining information extracted from multiple video sequences is considered. The main novelty of the proposed approach relies on the use of aesthetic features for automatically aggregating the inputs from different cameras in a unique video. While prior methodologies have separately addressed the issues of aesthetic feature extraction from videos and video orchestration, in this work we exploit selected features of a scene for automatically selecting the shots being characterized by the best aesthetic score. In order to evaluate the effectiveness of the proposed method, a subjective experiment has been carried out with experts from the audiovisual field. The achieved results are encouraging and show that there is space for improving the performances.

In this work, the problem of video orchestration performed by combining information extracted by multiple video sequences is considered. The novelty of the proposed approach relies on the use of aesthetic features and of cinematographic composition rules for automatically aggregating the inputs from different cameras in a unique video. While prior methodologies have separately addressed the issues of aesthetic feature extraction from videos and video orchestration, in this work we exploit a set of features of a scene for automatically selecting the shots being characterized by the best aesthetic score. In order to evaluate the effectiveness of the proposed method, a preliminary subjective experiment has been carried out with experts from the audiovisual field. The achieved results are encouraging and show that there is space for improving the performances.

In this paper, two image quality metrics based on morphological multiscale decompositions have been applied for the evaluation of free viewpoint video sequences. These sequences are synthesized using decompressed depth maps in the Depth-Image-Based Rendering synthesis process. Since the synthesis introduces edge distortion in the synthesized image/video, morphological filters are used for their ability to maintain important geometric information (i.e., edges) across different resolution levels. The edges displacement in different resolution scales is evaluated by means of the Mean Square Error. The adopted metrics show higher correlation with human judgment than state-of-the-art image quality measures used in this context.

In free-viewpoint rendering systems, one of the most challenging goals is the creation of virtual views based on available color texture (RGB) and depth data. Conventional depth-image-based rendering (DIBR) approaches have assumed that the virtual camera can only be displaced horizontally, thus leading to fairly simple disocclusion artifacts. However, in free-viewpoint DIBR, the virtual camera can be positioned in an arbitrary way and the respective disocclusion artifacts can exhibit complicated anisotropic appearances. Consequently, conventional approaches for compensating disocclusion holes usually fail in such arbitrary camera motion. We present a disocclusion compensation technique based on texture inpainting. We propose a layered representation of both the color and depth images in local foreground, background, and undefined segments (a trimap). This representation allows for employing an efficient alpha-matting approach for reconstructing the underlying opacity layer followed by a background compensation and layered rendering. The performance of the proposed method is evaluated with respect to the state-of-the-art through objective and subjective tests. The achieved results, especially for large camera displacements, outperform the state-of-the-art. Those results assess the effectiveness of the proposed method and highlight the need for new quality metrics able to address the impairments of this type of content.

In this contribution, the design of a Light Field image dataset is presented. It can be useful for design, testing, and benchmarking Light Field image processing algorithms. As first step, image content selection criteria have been defined based on selected image quality key-attributes, i.e. spatial information, colorfulness, texture key features, depth of field, etc. Next, image scenes have been selected and captured by using the Lytro Illum Light Field camera. Performed analysis shows that the proposed set of images is sufficient for addressing a wide range of attributes relevant for assessing Light Field image quality.

In this paper a multi-resolution depth field estimation algorithm for plenoptic cameras is presented. To face the potential accuracy losses originated from ambiguity problems arising in flat surface regions, still preserving bandwidth in correspondence of edges, a multi-resolution scheme is proposed. The achieved results show that the proposed local optimization method outperforms state of the art more complex global optimization based methods.

Securing multimedia data from undesired manipulation is a widely investigated topic in the state of the art. A large number of techniques has been developed for protecting images, videos, and also audio from malicious attacks. Nowadays new imaging systems pose new challenges from the data protection point of view. In particular, plenoptic cameras allow to record multiple views of a scene by using a single camera in a single shot, thus avoiding the problems related to calibration and camera synchronization. In this contribution, a novel embedding scheme tuned to light fields is presented. The watermark is inserted in the horizontal and vertical detail subbands of the first level of the Haar wavelet transform. The aim of this work is twofold: the design of a water-marking system that does not affect the depth map estimation procedure and therefore the refocusing procedure and the 3D scene reconstruction. On the other hand, the impact of light field data modification with respect to the perceived quality has been investigated. Experimental tests, both objective and subjective, have been carried out for assessing the performances of the proposed algorithm.

Face processing techniques for automatic recognition in broadcast video attract the research interest because of its value in applications, such as video indexing, retrieval, and summarization. In multimedia press review, the automatic annotation of broadcasting news programs is a challenging task because people can appear with large appearance variations such as hair styles, illumination conditions and poses that make the comparison between similar faces more difficult. In this paper a technique for automatic face identification in TV broadcasting programs based on a gallery of faces downloaded from Web is proposed. The approach is based on a joint use of Scale Invariant Feature Transform descriptor and Eigenfaces-based algorithms and it has been tested on video sequences using a database of images acquired starting from a web search. Experimental results show that the joint use of these two approaches improves the recognition rate in case of use Standard Definition (SD) and High Definition (HD) standards.

The use of 3D video is growing in several fields such as entertainment, military simulations, medical applications. However, the process of recording, transmitting, and processing 3D video is prone to errors thus producing artifacts that may affect the perceived quality. Nowadays a challenging task is the definition of a new metric able to predict the perceived quality with low computational complexity in order to be used in real-time applications. The research in this field is very active due to the complexity of the analysis of the influence of stereoscopic cues. In this paper we present a novel stereoscopic metric based on the combination of relevant features able to predict the subjective quality rating in a more accurate way. acceptedVersion

No-reference video quality assessment is a challenging task since the quality evaluation is not based on a priori knowledge of the original signal. In this paper we propose a no-reference quality metric for IP-based video transmission. The video quality is estimated based on the evaluation of the artifacts introduced by H.264/AVC coding and by the impairments caused by the IP transmission system. Performance experiments show the effectiveness of the proposed system in matching the subjective judgment. The collected scores have been compared with state of art video quality metrics.

In this contribution the design of a bi-technological enhanced terminal for secure mobile communication is presented. It is based on the TETRA and TETRAPOL communication networks, that are currently used by public and private bodies as network infrastructures and are particularly exploited in case of emergency. The proposed system allows the use of both, TETRA and TETRAPOL, low level communications layers. Furthermore, the proposed terminal introduces high level capabilities that enhance the basic functionalities of these communication systems, while preserving the intrinsics security of the standards. The implementation of the proposed terminal architecture is the basis for the development of an integrated European network where emergency services and first responders share communications, processes, and a legal framework, to be used in case of natural disasters and security threats. In this contribution, the terminal architecture and the security analysis are introduced and discussed.

In this paper a video database, ReTRiEVED, to be used in evaluating the performances of video quality metrics is presented. The database contains 184 distorted videos obtained from eight videos of different content. Packet loss rate, jitter, delay, and throughput have been considered as possible distortions resulting from video transmission. Video sequences, collected subjective scores, and results of the performed analysis are made publicly available for the research community, for designing, testing and comparing objective video quality metrics. The analysis of the results shows that packet loss rate, throughput/bandwidth, and jitter have significant effect on perceived quality, while an initial delay does not significantly affect the perceived quality.

Internet of Things (IoT) refers to the capability to connect, communicate and remotely manage a large number of networked, automated devices via the Internet. IoT is becoming as part of daily life and aims to extend pervasive communication and networking anytime, anywhere with any device. In this context security requirements and architectures must be properly formulated, implemented in order to enforce the security policies during their life-cycle. This paper provides a survey and analysis of security in the area of IoT introducing an approach addressed to overcome the conventional security solutions and deploy a federated architecture for dynamic prevention, detection, diagnosis, isolation, and countermeasures against cyber attacks. Based on the analysis of the most common web services, the paper defines the security needs proposing a federated model to design an architecture for secure exchange of services in IoT paradigm.

The automatic labeling of faces in TV broadcasting is still a challenging problem. The high variability in view points, facial expressions, general appearance, and lighting conditions, as well as occlusions, rapid shot changes, and camera motions, produce significant variations in image appearance. The application of automatic tools for face recognition is not yet fully established and the human intervention is needed. In this paper, we deal with the automatic face recognition in TV broadcasting programs. The target of the proposed method is to identify the presence of a specific person in a video by means of a set of images downloaded from Web using a specific search key.

Automatic annotation of broadcasting news programs is a challenging task for multimedia press review. Usually in a video stream, the head of a person moves continuously and changes in facial expressions, lighting conditions, and camera motion produce significant distortions in the image appearance that can largely affect recognition performances. In this paper a system for automatic face identification in TV broadcasting programs is proposed. The proposed approach is based on a joint use of Scale Invariant Feature Transform descriptor and Eigenfaces-based approach. The algorithm has been tested on two national broadcasting channels. Experimental results show that the joint use of these two approaches improves the recognition rate.

In this work an analysis of the influence of contrast modification on people’s opinion is performed. Based on a previous study of the impact of image contrast modifications on human feelings, here we address the problem of determining the impact of the image contrast change on the opinion of a person. This analysis is based on subjective tests that have been performed exploiting crowdsourcing. The obtained results show a good correlation with the previous work and they witness the possibility to influence subjective opinion by means of contrast modification. This study can results in forensics tools for blind detection of opinion biasing by means of image processing.

Scale Invariant Feature Transform (SIFT) has been widely employed in several image application domains, including Image Forensics (e.g. detection of copy-move forgery or near duplicates). Recently, a number of methods allowing to remove SIFT keypoints from an original image have been devised studying the problem of SIFT security against malicious procedures. Such techniques are quite effective in producing an attacked image with very few (or no) keypoints, but at the expense of an image distortion. Final perceptual quality has been taken in account very roughly so far. In this paper, effectiveness of the attacking methods is evaluated also from the side of perceptual image quality; a new version of a SIFT keypoint removal method, based on a perceptual metric, is presented and an extended series of perceptive experiments is reported.

International audience; In this paper we present a novel image quality assessment technique for evaluating virtual synthesized views in the context of multi-view video.In particular, Free Viewpoint Videos are generated from uncompressed color views and their compressed associated depth maps by means of the View Synthesis Reference Software, provided by MPEG. Prior to the synthesis step, the original depth maps are encoded with different coding algorithms thus leading to the creation of additional artifacts in the synthesized views. The core of proposed wavelet-based metric is in the registration procedure performed to align the synthesized view and the original one, and in the skin detection that has been applied considering that the same distortion is more annoying if visible on human subjects rather than on other parts of the scene. The effectiveness of the metric is evaluated by analyzing the correlation of the scores obtained with the proposed metric with Mean Opinion Scores collected by means of subjective tests. These results are also compared against those of well known objective quality metrics. The experimental results confirm the effectiveness of the proposed metric.

Person re-identification through a camera network deals with finding a correct link between consecutive observations of the same target among different cameras in order to choose the most probable correspondence among a set of possible matches. This task is particularly challenging in presence of low-resolution camera networks. In this work, a method for people re-identification in a framework of low-resolution camera network is presented. The proposed approach can be divided in two parts. First, the illumination changes of a target while crossing the network is analyzed. The color structure is evaluated using a novel color descriptor, the Color Structure Descriptor, which describes the differences of dominant colors between two regions of interest. Afterwards, a new pruning system for the links, the Target Color Structure is proposed. Results shows that the improvements achieved applying Target Color Structure control are up to 4% for the top rank and up to 16% considering the first eleven more similar candidates. © 2013 SPIE-ISandT.

"The use of ear information for people identification has been under testing at least for 100 years. However, it is still an open issue if the ears can be considered unique or unique enough to be used as biometric feature. In this paper a biometric system for human identification based on ear recognition is presented. The ear is modeled as a set of contours extracted from the ear image with an edge potential function. The matching algorithm has been tested in presence of several image modifications. Two human ear databases have been used for the tests. The experimental results show the effectiveness of the proposed scheme."

In this paper a methodology for digital image forgery detection by means of an unconventional use of image quality assessment is addressed. In particular, the presence of differences in quality degradations impairing the images is adopted to reveal the mixture of different source patches. The ratio behind this work is in the hypothesis that any image may be affected by artifacts, visible or not, caused by the processing steps: acquisition (i.e., lens distortion, acquisition sensors imperfections, analog to digital conversion, single sensor to color pattern interpolation), processing (i.e., quantization, storing, jpeg compression, sharpening, deblurring, enhancement), and rendering (i.e., image decoding, color/size adjustment). These defects are generally spatially localized and their strength strictly depends on the content. For these reasons they can be considered as a fingerprint of each digital image. The proposed approach relies on a combination of image quality assessment systems. The adopted no-reference metric does not require any information about the original image, thus allowing an efficient and stand-alone blind system for image forgery detection. The experimental results show the effectiveness of the proposed scheme.

In this paper we propose a new hybrid architecture between client/server and P2P network architectures in order to provide better service quality to the end-user. To distribute and protect the content, we use a discrete geometry tool as the Finite Radon Transform (FRT) in a Maximum Distance Separable (MDS) fashion. The presented results clarify the interest of the proposed architecture in terms of QoS.

'View plus depth' is an attractive compact representation format for 3D video compression and transmission. It combines 2D video with depth map sequence aligned in a per-pixel manner to represent the moving 3D scene in interest. Any different-perspective view can be synthesized out if this representation through Depth-Image Based Rendering (DIBR). However, such rendering is prone to disocclusion errors: regions originally covered by foreground objects become visible in the synthesized view and have to be filled with perceptually-meaningful data. In this work, a technique for reducing the perceived artifacts by inpainting the disoccluded areas is proposed. Based on Criminisi's exemplar-based inpainting algorithm, the developed technique recovers the disoccluded areas by using pixels of similar blocks surrounding it. In the original work, a moving window is centered on the boundaries between known and unknown parts ('target window'). The known pixels are used to select windows which are most similar to the target one. When this process is completed, the unknown region of the target patch is filled with a weighted combination of pixels from the selected windows. In the proposed scheme, the priority map, which defines the rule for selecting the order of pixels to be filled, has been modified to meet the requirement for disocclusion hole filling and a better non-local mean estimate has been suggested accordingly. Furthermore, the search for similar patches has also been extended to previous and following frames of the video under processing, thus improving both computational e±ciency and resulting quality. The effectiveness of the proposed method is demonstrated by objective and subjective tests.

The use of digital media for improving the effectiveness of teaching methodologies in Universities is increasing. In this contribution the experience performed at University of Roma TRE is presented. Since 2005 an online contest is used in the Multimedia class for improving both knowledge and human interaction. This paper shows the competitive-collaborative learning scheme adopted and it reports the feedback received by the students that has been collected by means of a questionnaire. The obtained comments show the effectiveness of the proposed learning scheme.

The increasing use of digital image-based applications is resulting in huge databases that are often difficult to use and prone to misuse and privacy concerns. These issues are especially crucial in medical applications. The most commonly adopted solution is the encryption of both the image and the patient data in separate files that are then linked. This practice results to be inefficient since, in order to retrieve patient data or analysis details, it is necessary to decrypt both files. In this contribution, an alternative solution for secure medical image annotation is presented. The proposed framework is based on the joint use of a key-dependent wavelet transform, the Integer Fibonacci-Haar transform, of a secure cryptographic scheme, and of a reversible watermarking scheme. The system allows: i) the insertion of the patient data into the encrypted image without requiring the knowledge of the original image, ii) the encryption of annotated images without causing loss in the embedded information, and iii) due to the complete reversibility of the process, it allows recovering the original image after the mark removal. Experimental results show the effectiveness of the proposed scheme.

In this paper a commutative watermarking and ciphering scheme for digital images is presented. The commutative property of the proposed method allows to cipher a watermarked image without interfering with the embedded signal or to watermark an encrypted image still allowing a perfect deciphering. Both operations are performed on a parametric transform domain: the Tree Structured Haar transform. The key dependence of the adopted transform domain increases the security of the overall system. In fact, without the knowledge of the generating key it is not possible to extract any useful information from the ciphered-watermarked image. Experimental results show the effectiveness of the proposed scheme.

"Low-cost personal computers, wireless access technologies, the Internet, and computer-equipped classrooms allow the design of novel and complementary methodologies for teaching digital information security in electrical engineering curricula. The challenges of the current digital information era require experts who are effectively able to counteract piracy, forgery, copyright infringement, and so on. Digital watermarking is one possible technique for fighting piracy, which consists of the insertion of invisible but robust information to protect the data. In this paper, a new teaching approach, designed for testing student skills and progressing in multimedia data protection, is presented. This consists of a distributed security game where students compete by first using the developed watermarking techniques and then attacking each other's methods, thus verifying their robustness. Groups of students from different universities and countries play against each other, trying to compromise other teams' hiding systems while protecting their own data from attacks. The proposed methodology can be considered as an appealing approach for stimulating learning, cooperation, and team competition. The effectiveness of the teaching method is verified by a student survey and their academic results."

Due to the recent success of 3D cinema, 3D video has been gathering considerable interest and delivery of 3D video to home TVs and mobile devices has been actively researched. A broadcast-friendly 3D video delivery format is so-called 'view plus depth', where single video sequence is augmented by dense depth information. Based on it, desired views can be synthesized at the receiver side. While well susceptible for effective compression, this representation presents problems related with occluded areas which become visible in the synthesized views. Different occlusion filling techniques have been developed varying in quality and complexity. This contribution aims at providing an objective and subjective comparison of three such techniques. These include adaptive pre-filtering of depth maps, and one simplified and one high-performance inpainting techniques. The latter two have been specifically modified for the case of occlusion filling. We have aimed especially at ranking the performance in the case of mobile 3D imaging. Along with objective comparisons, results from subjective tests are presented as well. Both groups of results demonstrate the superiority of the second inpainting technique, which is however also the most computationally expensive one.

In this contribution a novel method for video error control and concealment is proposed. The system is based on the use of data hiding techniques for transmitting the extra information needed to recover the data lost during transmission. The embedded data is a sub sampled binary version of each key frame of the shot. The embedding is performed by using the Quantization Index Modulation (QIM) scheme in the 3D wavelet transform domain. The experimental results show the effectiveness of the proposed approach. ©2010 IEEE.

In this paper a watermarking scheme based on the Human Visual System (HVS) is presented. A Quantization Index Modulation - Dither Modulation scheme is used in the Discrete Cosine Transform domain for the embedding. The watermark quantization step is selected according to the inter-frequency masking effect computed by using a HVS-inspired objective image quality metric. Experimental results demonstrate the effectiveness and the robustness of the proposed solution.

A novel method for watermarking and ciphering color images, based on the joint use of a key-dependent wavelet transform with a secure cryptographic scheme, is presented. The system allows to watermark encrypted data without requiring the knowledge of the original data and also to cipher watermarked data without damaging the embedded signal. Since different areas of the proposed transform domain are used for encryption and watermarking, the extraction of the hidden information can be performed without deciphering the cover data and it is also possible to decipher watermarked data without removing the watermark. Experimental results show the effectiveness of the proposed scheme.

This paper proposes a novel data hiding scheme in which a payload is embedded into the discrete cosine transform domain. The characteristics of the Human Visual System (HVS) with respect to image fruition have been exploited to adapt the strength of the embedded data and integrated in the design of a digital image watermarking system. By using an HVS-inspired image quality metric, we study the relation between the amount of data that can be embedded and the resulting perceived quality. This study allows one to increase the robustness of the watermarked image without damaging the perceived quality, or, as alternative, to reduce the impairments produced by the watermarking process given a fixed embedding strength. Experimental results show the effectiveness and the robustness of the proposed solution.

In this paper a novel method for watermarking and ciphering color images is presented. The aim of the system is to allow the watermarking of encrypted data without requiring the knowledge of the original data. By using this method, it is also possible to cipher watermarked data without damaging the embedded signal. Furthermore, the extraction of the hidden information can be performed without deciphering the cover data and it is also possible to decipher watermarked data without removing the watermark. The transform domain adopted in this work is the Fibonacci-Haar wavelet transform. The experimental results show the effectiveness of the proposed scheme. © 2008 SPIE-IS&T.

In this paper a joint watermarking and ciphering scheme for digital images is presented. Both operations are performed on a key-dependent transform domain. The commutative property of the proposed method allows to cipher a watermarked image without interfering with the embedded signal or to watermark an encrypted image still allowing a perfect deciphering. Furthermore, the key dependence of the transform domain increases the security of the overall system. Experimental results show the effectiveness of the proposed scheme.

In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.

In this contribution, we present a novel technique for imperceptible and robust watermarking of digital images. It is based on the host image 2-th level decomposition using the Fibonacci-Haar Transform (FHT) and on the Singular Value Decomposition (SVD) of the transformed subbands. The main contributions of this approach are the use of the FHT for hiding purposes, the flexibility in data hiding capacity, and the key-dependent secrecy of the used transform. The experimental results show the effectiveness of the proposed approach both in perceived quality of the watermarked image and in robustness against the most common attacks.